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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Sentiment Analysis</h1><div class="u-tcGrayDark">20,403 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Sentiment Analysis</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sentiment_Analysis/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sentiment_Analysis/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Sentiment_Analysis/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Sentiment_Analysis">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_64417565" data-work_id="64417565" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/64417565/Improving_Hate_Speech_Detection_of_Urdu_Tweets_Using_Sentiment_Analysis">Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_64417565" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/64417565" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7372c5c8d97448c011618188f199963a" rel="nofollow" data-download="{"attachment_id":76503536,"asset_id":64417565,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76503536/download_file?st=MTc0MDYwNTUzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="43445117" href="https://uet.academia.edu/SaharRauf">Sahar Rauf</a><script data-card-contents-for-user="43445117" type="text/json">{"id":43445117,"first_name":"Sahar","last_name":"Rauf","domain_name":"uet","page_name":"SaharRauf","display_name":"Sahar Rauf","profile_url":"https://uet.academia.edu/SaharRauf?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_64417565 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="64417565"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 64417565, container: ".js-paper-rank-work_64417565", }); });</script></li><li class="js-percentile-work_64417565 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64417565; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_64417565"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_64417565 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="64417565"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64417565; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64417565]").text(description); $(".js-view-count-work_64417565").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_64417565").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="64417565"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a><script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=64417565]'), work: {"id":64417565,"title":"Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis","created_at":"2021-12-15T22:21:43.522-08:00","url":"https://www.academia.edu/64417565/Improving_Hate_Speech_Detection_of_Urdu_Tweets_Using_Sentiment_Analysis?f_ri=5379","dom_id":"work_64417565","summary":"Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.","downloadable_attachments":[{"id":76503536,"asset_id":64417565,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":43445117,"first_name":"Sahar","last_name":"Rauf","domain_name":"uet","page_name":"SaharRauf","display_name":"Sahar Rauf","profile_url":"https://uet.academia.edu/SaharRauf?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379"},{"id":3581417,"name":"Hate Speech Detection ","url":"https://www.academia.edu/Documents/in/Hate_Speech_Detection?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2322048" data-work_id="2322048" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2322048/Integrating_DBpedia_and_SentiWordNet_for_a_tourism_recommender_system">Integrating DBpedia and SentiWordNet for a tourism recommender system</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The popularity of the social web introduces opportunities for the recommender systems, whilst new challenges arise when semantic knowledge is integrated in the landscape. The large amount of opinions available from Web 2.0 are exploited... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2322048" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The popularity of the social web introduces opportunities for the recommender systems, whilst new challenges arise when semantic knowledge is integrated in the landscape. The large amount of opinions available from Web 2.0 are exploited here to improve recommendation techniques in a semantic context. The developed recommendation system matches the crawled opinions against tourist objectives within the DBpedia ontology. Following a natural language processing step in Gate, several metrics are employed to build a recommendation plan, and formal justification is provided in case of need.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2322048" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6dafcba534a5a683fd3095c4ea572311" rel="nofollow" data-download="{"attachment_id":34027744,"asset_id":2322048,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34027744/download_file?st=MTc0MDYwNTUzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2946997" href="https://utcluj.academia.edu/AdrianGroza">Adrian Groza</a><script data-card-contents-for-user="2946997" type="text/json">{"id":2946997,"first_name":"Adrian","last_name":"Groza","domain_name":"utcluj","page_name":"AdrianGroza","display_name":"Adrian Groza","profile_url":"https://utcluj.academia.edu/AdrianGroza?f_ri=5379","photo":"https://0.academia-photos.com/2946997/973657/1564590/s65_adrian.groza.jpg"}</script></span></span></li><li class="js-paper-rank-work_2322048 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2322048"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2322048, container: ".js-paper-rank-work_2322048", }); });</script></li><li class="js-percentile-work_2322048 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2322048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_2322048"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_2322048 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="2322048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2322048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2322048]").text(description); $(".js-view-count-work_2322048").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_2322048").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="2322048"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="909" rel="nofollow" href="https://www.academia.edu/Documents/in/Tourism_Studies">Tourism Studies</a>, <script data-card-contents-for-ri="909" type="text/json">{"id":909,"name":"Tourism Studies","url":"https://www.academia.edu/Documents/in/Tourism_Studies?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2900" rel="nofollow" href="https://www.academia.edu/Documents/in/Recommender_Systems">Recommender Systems</a>, <script data-card-contents-for-ri="2900" type="text/json">{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="455746" rel="nofollow" href="https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS">OPINION MINING AND SENTIMENT ANALYSIS</a><script data-card-contents-for-ri="455746" type="text/json">{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2322048]'), work: {"id":2322048,"title":"Integrating DBpedia and SentiWordNet for a tourism recommender system","created_at":"2012-12-22T22:45:05.690-08:00","url":"https://www.academia.edu/2322048/Integrating_DBpedia_and_SentiWordNet_for_a_tourism_recommender_system?f_ri=5379","dom_id":"work_2322048","summary":"The popularity of the social web introduces opportunities for the recommender systems, whilst new challenges arise when semantic knowledge is integrated in the landscape. The large amount of opinions available from Web 2.0 are exploited here to improve recommendation techniques in a semantic context. The developed recommendation system matches the crawled opinions against tourist objectives within the DBpedia ontology. Following a natural language processing step in Gate, several metrics are employed to build a recommendation plan, and formal justification is provided in case of need.","downloadable_attachments":[{"id":34027744,"asset_id":2322048,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2946997,"first_name":"Adrian","last_name":"Groza","domain_name":"utcluj","page_name":"AdrianGroza","display_name":"Adrian Groza","profile_url":"https://utcluj.academia.edu/AdrianGroza?f_ri=5379","photo":"https://0.academia-photos.com/2946997/973657/1564590/s65_adrian.groza.jpg"}],"research_interests":[{"id":909,"name":"Tourism Studies","url":"https://www.academia.edu/Documents/in/Tourism_Studies?f_ri=5379","nofollow":true},{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76061491" data-work_id="76061491" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76061491/Sentiment_Analysis_for_Effective_Stock_Market_Prediction">Sentiment Analysis for Effective Stock Market Prediction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Stock market forecasters focus on developing a successful approach to predict stock prices. The vital idea to successful stock market prediction is not only achieving best results but also to minimize the inaccurate forecast of stock... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_76061491" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Stock market forecasters focus on developing a successful approach to predict stock prices. The vital idea to successful stock market prediction is not only achieving best results but also to minimize the inaccurate forecast of stock prices. This paper attempts to design and implement a predictive system for guiding stock market investment. The novelty of our approach is the combination of both sensex points and Really Simple Syndication (RSS) feeds for effective prediction. Our claim is that the sentiment analysis of RSS news feeds has an impact on stock market values. Hence RSS news feed data are collected along with the stock market investment data for a period of time. Using our algorithm for sentiment analysis, the correlation between the stock market values and sentiments in RSS news feeds are established. This trained model is used for prediction of stock market rates. In our experimental study the stock market prices and RSS news feeds are collected for the company ARBK from Amman Stock Exchange (ASE). Our experimental study has shown an improvement of 14.43% accuracy prediction, when compared with the standard algorithm of ID3, C4.5 and moving average stock level indicator.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76061491" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ae7ea66e141438792f6a0eb9eaf825a9" rel="nofollow" data-download="{"attachment_id":83746612,"asset_id":76061491,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83746612/download_file?st=MTc0MDYwNTUzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="184631562" href="https://hindustanuniv.academia.edu/DrShriBharathiSVAssistantProfessorIIICSE">Dr. Shri Bharathi SV</a><script data-card-contents-for-user="184631562" type="text/json">{"id":184631562,"first_name":"Dr. Shri Bharathi","last_name":"SV","domain_name":"hindustanuniv","page_name":"DrShriBharathiSVAssistantProfessorIIICSE","display_name":"Dr. Shri Bharathi SV","profile_url":"https://hindustanuniv.academia.edu/DrShriBharathiSVAssistantProfessorIIICSE?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_76061491 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76061491"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76061491, container: ".js-paper-rank-work_76061491", }); 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$(".js-view-count[data-work-id=76061491]").text(description); $(".js-view-count-work_76061491").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_76061491").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="76061491"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14494" rel="nofollow" href="https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_">Opinion Mining (Data Mining)</a><script data-card-contents-for-ri="14494" type="text/json">{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=76061491]'), work: {"id":76061491,"title":"Sentiment Analysis for Effective Stock Market Prediction","created_at":"2022-04-10T22:07:32.650-07:00","url":"https://www.academia.edu/76061491/Sentiment_Analysis_for_Effective_Stock_Market_Prediction?f_ri=5379","dom_id":"work_76061491","summary":"The Stock market forecasters focus on developing a successful approach to predict stock prices. The vital idea to successful stock market prediction is not only achieving best results but also to minimize the inaccurate forecast of stock prices. This paper attempts to design and implement a predictive system for guiding stock market investment. The novelty of our approach is the combination of both sensex points and Really Simple Syndication (RSS) feeds for effective prediction. Our claim is that the sentiment analysis of RSS news feeds has an impact on stock market values. Hence RSS news feed data are collected along with the stock market investment data for a period of time. Using our algorithm for sentiment analysis, the correlation between the stock market values and sentiments in RSS news feeds are established. This trained model is used for prediction of stock market rates. In our experimental study the stock market prices and RSS news feeds are collected for the company ARBK from Amman Stock Exchange (ASE). Our experimental study has shown an improvement of 14.43% accuracy prediction, when compared with the standard algorithm of ID3, C4.5 and moving average stock level indicator.","downloadable_attachments":[{"id":83746612,"asset_id":76061491,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":184631562,"first_name":"Dr. Shri Bharathi","last_name":"SV","domain_name":"hindustanuniv","page_name":"DrShriBharathiSVAssistantProfessorIIICSE","display_name":"Dr. Shri Bharathi SV","profile_url":"https://hindustanuniv.academia.edu/DrShriBharathiSVAssistantProfessorIIICSE?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=5379","nofollow":true},{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=5379"},{"id":140576,"name":"Social Media Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Social_Media_Sentiment_Analysis?f_ri=5379"},{"id":161976,"name":"Stock Market Technical Analysis","url":"https://www.academia.edu/Documents/in/Stock_Market_Technical_Analysis?f_ri=5379"},{"id":193914,"name":"Stock Market Prediction","url":"https://www.academia.edu/Documents/in/Stock_Market_Prediction?f_ri=5379"},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379"},{"id":2552564,"name":"Stock Market Forecasting","url":"https://www.academia.edu/Documents/in/Stock_Market_Forecasting?f_ri=5379"},{"id":2788169,"name":"RSS News feeds","url":"https://www.academia.edu/Documents/in/RSS_News_feeds?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_70786404" data-work_id="70786404" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/70786404/Combining_strengths_emotions_and_polarities_for_boosting_Twitter_sentiment_analysis">Combining strengths, emotions and polarities for boosting Twitter sentiment analysis</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70786404" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="170849124" href="https://independent.academia.edu/marcelomendoza77">marcelo mendoza</a><script data-card-contents-for-user="170849124" type="text/json">{"id":170849124,"first_name":"marcelo","last_name":"mendoza","domain_name":"independent","page_name":"marcelomendoza77","display_name":"marcelo mendoza","profile_url":"https://independent.academia.edu/marcelomendoza77?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_70786404 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70786404"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70786404, container: ".js-paper-rank-work_70786404", }); 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Sentiment and emotion are interchangeably used, however have different meanings. The sentiment is a mental attitude or a thought influenced by emotion and has... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70131398" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiments are mental attitudes and play an important role in forming opinions. Sentiment and emotion are interchangeably used, however have different meanings. The sentiment is a mental attitude or a thought influenced by emotion and has an important role in forming opinions and influencing future decisions of others. Employees are knowledgeable assets for any organization, and their sentiments about their organization, managers, co-workers, etc. create an opinion. The emotional well-being of employees has a direct relationship with the performance of the organization. The World Health Organization declared COVID-19 as a pandemic on March 11, 2020. Many countries declared lockdowns, with unimaginable restrictions to control the spread of this pandemic. This resulted in organizations swiftly adapting to work-fromhome methods, almost overnight. This paper studies the sentiments and emotional wellbeing of employees during the COVID-19 pandemic. The study was conducted undertaking a review of the literature and adopting a framework for sentiment analysis and emotional wellbeing. The study shows that employees have mixed sentiments of feeling excited, positive, anxious, angry, and negative. Most of the participants with positive and excited sentiments believe that this pandemic has created a challenge as well as an opportunity.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70131398" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1066e1fd6985221b90ae2e8056604ae7" rel="nofollow" data-download="{"attachment_id":79990989,"asset_id":70131398,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79990989/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="143250315" href="https://uou.academia.edu/SPant">Sudhir Pant</a><script data-card-contents-for-user="143250315" type="text/json">{"id":143250315,"first_name":"Sudhir","last_name":"Pant","domain_name":"uou","page_name":"SPant","display_name":"Sudhir Pant","profile_url":"https://uou.academia.edu/SPant?f_ri=5379","photo":"https://0.academia-photos.com/143250315/50672528/38708144/s65_sudhir.pant.jpg"}</script></span></span></li><li class="js-paper-rank-work_70131398 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70131398"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70131398, container: ".js-paper-rank-work_70131398", }); 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$(".js-view-count[data-work-id=70131398]").text(description); $(".js-view-count-work_70131398").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_70131398").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="70131398"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="416422" rel="nofollow" href="https://www.academia.edu/Documents/in/Employee_Performance">Employee Performance</a>, <script data-card-contents-for-ri="416422" type="text/json">{"id":416422,"name":"Employee Performance","url":"https://www.academia.edu/Documents/in/Employee_Performance?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1421329" rel="nofollow" href="https://www.academia.edu/Documents/in/Emotional_Wellbeing">Emotional Wellbeing</a><script data-card-contents-for-ri="1421329" type="text/json">{"id":1421329,"name":"Emotional Wellbeing","url":"https://www.academia.edu/Documents/in/Emotional_Wellbeing?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=70131398]'), work: {"id":70131398,"title":"A Study of Sentiments of Employees during COVID-19","created_at":"2022-01-31T03:05:26.183-08:00","url":"https://www.academia.edu/70131398/A_Study_of_Sentiments_of_Employees_during_COVID_19?f_ri=5379","dom_id":"work_70131398","summary":"Sentiments are mental attitudes and play an important role in forming opinions. Sentiment and emotion are interchangeably used, however have different meanings. The sentiment is a mental attitude or a thought influenced by emotion and has an important role in forming opinions and influencing future decisions of others. Employees are knowledgeable assets for any organization, and their sentiments about their organization, managers, co-workers, etc. create an opinion. The emotional well-being of employees has a direct relationship with the performance of the organization. The World Health Organization declared COVID-19 as a pandemic on March 11, 2020. Many countries declared lockdowns, with unimaginable restrictions to control the spread of this pandemic. This resulted in organizations swiftly adapting to work-fromhome methods, almost overnight. This paper studies the sentiments and emotional wellbeing of employees during the COVID-19 pandemic. The study was conducted undertaking a review of the literature and adopting a framework for sentiment analysis and emotional wellbeing. The study shows that employees have mixed sentiments of feeling excited, positive, anxious, angry, and negative. Most of the participants with positive and excited sentiments believe that this pandemic has created a challenge as well as an opportunity.","downloadable_attachments":[{"id":79990989,"asset_id":70131398,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":143250315,"first_name":"Sudhir","last_name":"Pant","domain_name":"uou","page_name":"SPant","display_name":"Sudhir Pant","profile_url":"https://uou.academia.edu/SPant?f_ri=5379","photo":"https://0.academia-photos.com/143250315/50672528/38708144/s65_sudhir.pant.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":416422,"name":"Employee Performance","url":"https://www.academia.edu/Documents/in/Employee_Performance?f_ri=5379","nofollow":true},{"id":1421329,"name":"Emotional Wellbeing","url":"https://www.academia.edu/Documents/in/Emotional_Wellbeing?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_60564683" data-work_id="60564683" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/60564683/Validating_the_Coverage_of_Lexical_Resources_for_Affect_Analysis_and_Automatically_Classifying_New_Words_along_Semantic_Axes">Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received much attention in computational linguistics until recently. But now that messages (including spam) have become... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_60564683" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received much attention in computational linguistics until recently. But now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this dimension is becoming more important. One resource needed for identifying affect in text is a lexicon of words with emotion-conveying potential. Starting from an existing affect lexicon and lexical patterns that invoke affect, we gathered a large quantity of text to measure the coverage of our existing lexicon. This article reports on our methods for identifying candidate affect words and our evaluation of our current affect lexicons. We describe how our affect lexicon can be extended based on results from these experiments.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/60564683" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6c1a84711f685c7e949fe18fa9e93f78" rel="nofollow" data-download="{"attachment_id":73953079,"asset_id":60564683,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/73953079/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="263990" href="https://independent.academia.edu/JamesShanahan">James Shanahan</a><script data-card-contents-for-user="263990" type="text/json">{"id":263990,"first_name":"James","last_name":"Shanahan","domain_name":"independent","page_name":"JamesShanahan","display_name":"James Shanahan","profile_url":"https://independent.academia.edu/JamesShanahan?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_60564683 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="60564683"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 60564683, container: ".js-paper-rank-work_60564683", }); });</script></li><li class="js-percentile-work_60564683 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 60564683; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_60564683"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_60564683 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="60564683"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60564683; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=60564683]").text(description); $(".js-view-count-work_60564683").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_60564683").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="60564683"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3268" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Linguistics">Computational Linguistics</a>, <script data-card-contents-for-ri="3268" type="text/json">{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="16337" rel="nofollow" href="https://www.academia.edu/Documents/in/Lexicography">Lexicography</a>, <script data-card-contents-for-ri="16337" type="text/json">{"id":16337,"name":"Lexicography","url":"https://www.academia.edu/Documents/in/Lexicography?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="21498" rel="nofollow" href="https://www.academia.edu/Documents/in/Affect_Emotion">Affect/Emotion</a><script data-card-contents-for-ri="21498" type="text/json">{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=60564683]'), work: {"id":60564683,"title":"Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes","created_at":"2021-10-31T08:55:28.583-07:00","url":"https://www.academia.edu/60564683/Validating_the_Coverage_of_Lexical_Resources_for_Affect_Analysis_and_Automatically_Classifying_New_Words_along_Semantic_Axes?f_ri=5379","dom_id":"work_60564683","summary":"In addition to factual content, many texts contain an emotional dimension. This emotive, or affect, dimension has not received much attention in computational linguistics until recently. But now that messages (including spam) have become more prevalent than edited texts (such as newswire), recognizing this dimension is becoming more important. One resource needed for identifying affect in text is a lexicon of words with emotion-conveying potential. Starting from an existing affect lexicon and lexical patterns that invoke affect, we gathered a large quantity of text to measure the coverage of our existing lexicon. This article reports on our methods for identifying candidate affect words and our evaluation of our current affect lexicons. We describe how our affect lexicon can be extended based on results from these experiments.","downloadable_attachments":[{"id":73953079,"asset_id":60564683,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":263990,"first_name":"James","last_name":"Shanahan","domain_name":"independent","page_name":"JamesShanahan","display_name":"James Shanahan","profile_url":"https://independent.academia.edu/JamesShanahan?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":16337,"name":"Lexicography","url":"https://www.academia.edu/Documents/in/Lexicography?f_ri=5379","nofollow":true},{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true},{"id":29789,"name":"Computational linguistic phylogenetics","url":"https://www.academia.edu/Documents/in/Computational_linguistic_phylogenetics?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_53092203" data-work_id="53092203" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/53092203/A_Combined_Weighting_for_the_Feature_Based_Method_on_Topological_Parameters_in_Semantic_Taxonomy_Using_Social_Media">A Combined Weighting for the Feature-Based Method on Topological Parameters in Semantic Taxonomy Using Social Media</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_53092203" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and<br />effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate<br />the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined<br />four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the<br />proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/53092203" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="26176cfea2f113c78831257a01d1357b" rel="nofollow" data-download="{"attachment_id":70041374,"asset_id":53092203,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/70041374/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="203541451" href="https://ump.academia.edu/AliMHasan">Ali M. Hasan</a><script data-card-contents-for-user="203541451" type="text/json">{"id":203541451,"first_name":"Ali","last_name":"M. Hasan","domain_name":"ump","page_name":"AliMHasan","display_name":"Ali M. Hasan","profile_url":"https://ump.academia.edu/AliMHasan?f_ri=5379","photo":"https://0.academia-photos.com/203541451/64475800/52798066/s65_ali.m._hasan.jpg"}</script></span></span></li><li class="js-paper-rank-work_53092203 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="53092203"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 53092203, container: ".js-paper-rank-work_53092203", }); });</script></li><li class="js-percentile-work_53092203 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 53092203; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_53092203"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_53092203 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="53092203"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 53092203; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=53092203]").text(description); $(".js-view-count-work_53092203").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_53092203").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="53092203"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5456" rel="nofollow" href="https://www.academia.edu/Documents/in/Software_Components">Software Components</a>, <script data-card-contents-for-ri="5456" type="text/json">{"id":5456,"name":"Software Components","url":"https://www.academia.edu/Documents/in/Software_Components?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5639" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Mining">Text Mining</a><script data-card-contents-for-ri="5639" type="text/json">{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=53092203]'), work: {"id":53092203,"title":"A Combined Weighting for the Feature-Based Method on Topological Parameters in Semantic Taxonomy Using Social Media","created_at":"2021-09-21T02:03:18.363-07:00","url":"https://www.academia.edu/53092203/A_Combined_Weighting_for_the_Feature_Based_Method_on_Topological_Parameters_in_Semantic_Taxonomy_Using_Social_Media?f_ri=5379","dom_id":"work_53092203","summary":"The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and\neffective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate\nthe various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined\nfour things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the\nproposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.","downloadable_attachments":[{"id":70041374,"asset_id":53092203,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":203541451,"first_name":"Ali","last_name":"M. Hasan","domain_name":"ump","page_name":"AliMHasan","display_name":"Ali M. Hasan","profile_url":"https://ump.academia.edu/AliMHasan?f_ri=5379","photo":"https://0.academia-photos.com/203541451/64475800/52798066/s65_ali.m._hasan.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":5456,"name":"Software Components","url":"https://www.academia.edu/Documents/in/Software_Components?f_ri=5379","nofollow":true},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification?f_ri=5379"},{"id":140576,"name":"Social Media Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Social_Media_Sentiment_Analysis?f_ri=5379"},{"id":456942,"name":"Semantic representation","url":"https://www.academia.edu/Documents/in/Semantic_representation?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44359692" data-work_id="44359692" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44359692/ANALISIS_OPINI_TERHADAP_DC_UNIVERSE_PADA_MEDIA_SOSIAL_TWITTER_MENGGUNAKAN_METODE_NA%C3%8FVE_BAYES">ANALISIS OPINI TERHADAP DC UNIVERSE PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE NAÏVE BAYES</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44359692" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that produces and displays superheroes and super villains. To start a super hero-themed business, there are a number of business examples that can be used as references and can also be used to reap profits from the business, namely, rental of comics, selling merchandise, making clothing lines, making cosplay costumes, making superhero-themed foods, selling action figure. In an effort to start a super hero-themed business, especially the DC Universe theme, it is necessary to pay attention / listen to DC Universe consumers in Indonesia. Classification of opinions or sentiment analysis is one way to find out about a person or group of people towards certain products, services, issues or groups from various social media platforms and the internet. Twitter is one of the social media that is loved by the people of Indonesia. This research tries to utilize what was written by Twitter social media users or better known as a tweet. Tweets will be processed by text mining and processed again using the Naïve Bayes Classifier algorithm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44359692" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="83e997ad624707db667866f02dea4569" rel="nofollow" data-download="{"attachment_id":64755265,"asset_id":44359692,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64755265/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="10338940" href="https://amikom.academia.edu/AndrewPatrickdeFretes">Andrew Patrick de Fretes</a><script data-card-contents-for-user="10338940" type="text/json">{"id":10338940,"first_name":"Andrew Patrick","last_name":"de Fretes","domain_name":"amikom","page_name":"AndrewPatrickdeFretes","display_name":"Andrew Patrick de Fretes","profile_url":"https://amikom.academia.edu/AndrewPatrickdeFretes?f_ri=5379","photo":"https://0.academia-photos.com/10338940/10661672/35860738/s65_andrew_patrick.de_fretes.jpg"}</script></span></span></li><li class="js-paper-rank-work_44359692 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44359692"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44359692, container: ".js-paper-rank-work_44359692", }); 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$(".js-view-count[data-work-id=44359692]").text(description); $(".js-view-count-work_44359692").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44359692").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44359692"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5639" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Mining">Text Mining</a>, <script data-card-contents-for-ri="5639" type="text/json">{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="119217" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Mining_and_Information_Retrieval">Text Mining and Information Retrieval</a>, <script data-card-contents-for-ri="119217" type="text/json">{"id":119217,"name":"Text Mining and Information Retrieval","url":"https://www.academia.edu/Documents/in/Text_Mining_and_Information_Retrieval?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="145307" rel="nofollow" href="https://www.academia.edu/Documents/in/New_Media_Social_Network_Analysis_e-research_Link_analysis_Social_Network_Sites_Twitter_Facebo">New Media, Social Network Analysis, e-research, Link analysis, Social Network Sites, Twitter, Facebook, Political Communication</a><script data-card-contents-for-ri="145307" type="text/json">{"id":145307,"name":"New Media, Social Network Analysis, e-research, Link analysis, Social Network Sites, Twitter, Facebook, Political Communication","url":"https://www.academia.edu/Documents/in/New_Media_Social_Network_Analysis_e-research_Link_analysis_Social_Network_Sites_Twitter_Facebo?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44359692]'), work: {"id":44359692,"title":"ANALISIS OPINI TERHADAP DC UNIVERSE PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE NAÏVE BAYES","created_at":"2020-10-23T20:19:06.525-07:00","url":"https://www.academia.edu/44359692/ANALISIS_OPINI_TERHADAP_DC_UNIVERSE_PADA_MEDIA_SOSIAL_TWITTER_MENGGUNAKAN_METODE_NA%C3%8FVE_BAYES?f_ri=5379","dom_id":"work_44359692","summary":"DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that produces and displays superheroes and super villains. To start a super hero-themed business, there are a number of business examples that can be used as references and can also be used to reap profits from the business, namely, rental of comics, selling merchandise, making clothing lines, making cosplay costumes, making superhero-themed foods, selling action figure. In an effort to start a super hero-themed business, especially the DC Universe theme, it is necessary to pay attention / listen to DC Universe consumers in Indonesia. Classification of opinions or sentiment analysis is one way to find out about a person or group of people towards certain products, services, issues or groups from various social media platforms and the internet. Twitter is one of the social media that is loved by the people of Indonesia. This research tries to utilize what was written by Twitter social media users or better known as a tweet. Tweets will be processed by text mining and processed again using the Naïve Bayes Classifier algorithm.","downloadable_attachments":[{"id":64755265,"asset_id":44359692,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":10338940,"first_name":"Andrew Patrick","last_name":"de Fretes","domain_name":"amikom","page_name":"AndrewPatrickdeFretes","display_name":"Andrew Patrick de Fretes","profile_url":"https://amikom.academia.edu/AndrewPatrickdeFretes?f_ri=5379","photo":"https://0.academia-photos.com/10338940/10661672/35860738/s65_andrew_patrick.de_fretes.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true},{"id":119217,"name":"Text Mining and Information Retrieval","url":"https://www.academia.edu/Documents/in/Text_Mining_and_Information_Retrieval?f_ri=5379","nofollow":true},{"id":145307,"name":"New Media, Social Network Analysis, e-research, Link analysis, Social Network Sites, Twitter, Facebook, Political Communication","url":"https://www.academia.edu/Documents/in/New_Media_Social_Network_Analysis_e-research_Link_analysis_Social_Network_Sites_Twitter_Facebo?f_ri=5379","nofollow":true},{"id":345767,"name":"Naive Bayes","url":"https://www.academia.edu/Documents/in/Naive_Bayes?f_ri=5379"},{"id":459269,"name":"Data mining and Text mining","url":"https://www.academia.edu/Documents/in/Data_mining_and_Text_mining?f_ri=5379"},{"id":2002821,"name":"Naïve Bayes Learning Algorithm","url":"https://www.academia.edu/Documents/in/Na%C3%AFve_Bayes_Learning_Algorithm?f_ri=5379"},{"id":3126567,"name":"DC Cinematic Universe","url":"https://www.academia.edu/Documents/in/DC_Cinematic_Universe?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75326145" data-work_id="75326145" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75326145/IDENTIFICATION_OF_PUBLIC_SENTIMENT_OVER_COMMENTS_THROUGH_TWEETS_BY_DIGITAL_INDIA">IDENTIFICATION OF PUBLIC SENTIMENT OVER COMMENTS THROUGH TWEETS BY DIGITAL INDIA</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Digital India initiative by the Government of India is an initiative by Shri Narendra Modi, the Prime Minister of India. Launched in the year 2015, the programme enhanced its scope to various digital services wings. In this study, the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75326145" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Digital India initiative by the Government of India is an initiative by Shri Narendra Modi, the Prime Minister of India. Launched in the year 2015, the programme enhanced its scope to various digital services wings. In this study, the researcher attempted to identify public sentiments by studying their comments through Tweets. The researcher considered the Tweets by Digital India for a period of one year</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75326145" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dc82dd837e6ce6e5a4ce7b92b30f8361" rel="nofollow" data-download="{"attachment_id":83139168,"asset_id":75326145,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83139168/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="202033622" href="https://independent.academia.edu/AmritaChakraborty36">Amrita Chakraborty</a><script data-card-contents-for-user="202033622" type="text/json">{"id":202033622,"first_name":"Amrita","last_name":"Chakraborty","domain_name":"independent","page_name":"AmritaChakraborty36","display_name":"Amrita Chakraborty","profile_url":"https://independent.academia.edu/AmritaChakraborty36?f_ri=5379","photo":"https://0.academia-photos.com/202033622/77716712/66259717/s65_amrita.chakraborty.jpg"}</script></span></span></li><li class="js-paper-rank-work_75326145 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75326145"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75326145, container: ".js-paper-rank-work_75326145", }); });</script></li><li class="js-percentile-work_75326145 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75326145; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_75326145"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_75326145 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="75326145"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75326145; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75326145]").text(description); $(".js-view-count-work_75326145").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75326145").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75326145"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="933" rel="nofollow" href="https://www.academia.edu/Documents/in/New_Media">New Media</a>, <script data-card-contents-for-ri="933" type="text/json">{"id":933,"name":"New Media","url":"https://www.academia.edu/Documents/in/New_Media?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4986" rel="nofollow" href="https://www.academia.edu/Documents/in/Public_Relations">Public Relations</a>, <script data-card-contents-for-ri="4986" type="text/json">{"id":4986,"name":"Public Relations","url":"https://www.academia.edu/Documents/in/Public_Relations?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13873" rel="nofollow" href="https://www.academia.edu/Documents/in/Twitter">Twitter</a><script data-card-contents-for-ri="13873" type="text/json">{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75326145]'), work: {"id":75326145,"title":"IDENTIFICATION OF PUBLIC SENTIMENT OVER COMMENTS THROUGH TWEETS BY DIGITAL INDIA","created_at":"2022-04-03T08:59:37.974-07:00","url":"https://www.academia.edu/75326145/IDENTIFICATION_OF_PUBLIC_SENTIMENT_OVER_COMMENTS_THROUGH_TWEETS_BY_DIGITAL_INDIA?f_ri=5379","dom_id":"work_75326145","summary":"The Digital India initiative by the Government of India is an initiative by Shri Narendra Modi, the Prime Minister of India. Launched in the year 2015, the programme enhanced its scope to various digital services wings. In this study, the researcher attempted to identify public sentiments by studying their comments through Tweets. The researcher considered the Tweets by Digital India for a period of one year","downloadable_attachments":[{"id":83139168,"asset_id":75326145,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":202033622,"first_name":"Amrita","last_name":"Chakraborty","domain_name":"independent","page_name":"AmritaChakraborty36","display_name":"Amrita Chakraborty","profile_url":"https://independent.academia.edu/AmritaChakraborty36?f_ri=5379","photo":"https://0.academia-photos.com/202033622/77716712/66259717/s65_amrita.chakraborty.jpg"}],"research_interests":[{"id":933,"name":"New Media","url":"https://www.academia.edu/Documents/in/New_Media?f_ri=5379","nofollow":true},{"id":4986,"name":"Public Relations","url":"https://www.academia.edu/Documents/in/Public_Relations?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true},{"id":21388,"name":"E-Governance","url":"https://www.academia.edu/Documents/in/E-Governance?f_ri=5379"},{"id":333409,"name":"Digital Governance","url":"https://www.academia.edu/Documents/in/Digital_Governance?f_ri=5379"},{"id":1436826,"name":"Twitter Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Twitter_Sentiment_Analysis?f_ri=5379"},{"id":1512247,"name":"Digital India","url":"https://www.academia.edu/Documents/in/Digital_India?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66834891" data-work_id="66834891" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66834891/Multi_aspect_Sentiment_Analysis_with_Topic_Models">Multi-aspect Sentiment Analysis with Topic Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66834891" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge-in the form of seed words-to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66834891" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="be9044e038dbcdb8f69b948e023ccaf0" rel="nofollow" data-download="{"attachment_id":77876658,"asset_id":66834891,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77876658/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="194980044" href="https://independent.academia.edu/BinLu25">Bin Lu</a><script data-card-contents-for-user="194980044" type="text/json">{"id":194980044,"first_name":"Bin","last_name":"Lu","domain_name":"independent","page_name":"BinLu25","display_name":"Bin Lu","profile_url":"https://independent.academia.edu/BinLu25?f_ri=5379","photo":"https://0.academia-photos.com/194980044/57409149/45626950/s65_bin.lu.jpeg"}</script></span></span></li><li class="js-paper-rank-work_66834891 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66834891"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66834891, container: ".js-paper-rank-work_66834891", }); 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$(".js-view-count[data-work-id=66834891]").text(description); $(".js-view-count-work_66834891").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66834891").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66834891"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1283" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Security">Information Security</a>, <script data-card-contents-for-ri="1283" type="text/json">{"id":1283,"name":"Information Security","url":"https://www.academia.edu/Documents/in/Information_Security?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4252" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Networks">Computer Networks</a>, <script data-card-contents-for-ri="4252" type="text/json">{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66834891]'), work: {"id":66834891,"title":"Multi-aspect Sentiment Analysis with Topic Models","created_at":"2022-01-01T23:58:57.391-08:00","url":"https://www.academia.edu/66834891/Multi_aspect_Sentiment_Analysis_with_Topic_Models?f_ri=5379","dom_id":"work_66834891","summary":"We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge-in the form of seed words-to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that ...","downloadable_attachments":[{"id":77876658,"asset_id":66834891,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":194980044,"first_name":"Bin","last_name":"Lu","domain_name":"independent","page_name":"BinLu25","display_name":"Bin Lu","profile_url":"https://independent.academia.edu/BinLu25?f_ri=5379","photo":"https://0.academia-photos.com/194980044/57409149/45626950/s65_bin.lu.jpeg"}],"research_interests":[{"id":1283,"name":"Information Security","url":"https://www.academia.edu/Documents/in/Information_Security?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":92738,"name":"Text Analysis","url":"https://www.academia.edu/Documents/in/Text_Analysis?f_ri=5379"},{"id":115676,"name":"Cyber Security","url":"https://www.academia.edu/Documents/in/Cyber_Security?f_ri=5379"},{"id":313752,"name":"Gold Standard","url":"https://www.academia.edu/Documents/in/Gold_Standard?f_ri=5379"},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_49259142" data-work_id="49259142" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/49259142/A_SENTIMENT_ANALYSIS_OF_AIRLINE_SYSTEM_USING_MACHINE_LEARNING_ALGORITHMS">A SENTIMENT ANALYSIS OF AIRLINE SYSTEM USING MACHINE LEARNING ALGORITHMS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Twitter is the popular and commonly used social networking platform because it permits users to express their thoughts, opinions about any item, and allows them to post comments or messages all around the world. Sentiment Analysis... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_49259142" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Twitter is the popular and commonly used social networking platform because it permits users to express their thoughts, opinions about any item, and allows them to post comments or messages all around the world. Sentiment Analysis techniques are used to study and analyze these reviews or opinions. Sentiment analysis is a NLP technique that is used to express opinions into dif erent sentiments like positive, negative, and neutral. In this paper, we take Airline Dataset from Twitter and did sentiment analysis on that dataset using machine learning algorithms like SVM, Naïve Bayes and Random Forest. Sentiments are expressed in three categories positive, negative and neutral. Our dataset contains 11533 tweets and the dataset is not balanced. The performance of various machine learning algorithms is discussed in this paper</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/49259142" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2cfc5167c9c5c1a9ae41539875d8f0f2" rel="nofollow" data-download="{"attachment_id":67642966,"asset_id":49259142,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67642966/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39122404" href="https://iaeme.academia.edu/publication">IAEME Publication</a><script data-card-contents-for-user="39122404" type="text/json">{"id":39122404,"first_name":"IAEME","last_name":"Publication","domain_name":"iaeme","page_name":"publication","display_name":"IAEME Publication","profile_url":"https://iaeme.academia.edu/publication?f_ri=5379","photo":"https://0.academia-photos.com/39122404/12178523/13563629/s65_iaeme.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_49259142 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="49259142"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 49259142, container: ".js-paper-rank-work_49259142", }); });</script></li><li class="js-percentile-work_49259142 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 49259142; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_49259142"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_49259142 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="49259142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49259142; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=49259142]").text(description); $(".js-view-count-work_49259142").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_49259142").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="49259142"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13873" rel="nofollow" href="https://www.academia.edu/Documents/in/Twitter">Twitter</a>, <script data-card-contents-for-ri="13873" type="text/json">{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="70995" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Forest">Random Forest</a>, <script data-card-contents-for-ri="70995" type="text/json">{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="315668" href="https://www.academia.edu/Documents/in/Svm">Svm</a><script data-card-contents-for-ri="315668" type="text/json">{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm?f_ri=5379","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=49259142]'), work: {"id":49259142,"title":"A SENTIMENT ANALYSIS OF AIRLINE SYSTEM USING MACHINE LEARNING ALGORITHMS","created_at":"2021-06-16T02:09:51.027-07:00","url":"https://www.academia.edu/49259142/A_SENTIMENT_ANALYSIS_OF_AIRLINE_SYSTEM_USING_MACHINE_LEARNING_ALGORITHMS?f_ri=5379","dom_id":"work_49259142","summary":"Twitter is the popular and commonly used social networking platform because it permits users to express their thoughts, opinions about any item, and allows them to post comments or messages all around the world. Sentiment Analysis techniques are used to study and analyze these reviews or opinions. Sentiment analysis is a NLP technique that is used to express opinions into dif erent sentiments like positive, negative, and neutral. In this paper, we take Airline Dataset from Twitter and did sentiment analysis on that dataset using machine learning algorithms like SVM, Naïve Bayes and Random Forest. Sentiments are expressed in three categories positive, negative and neutral. Our dataset contains 11533 tweets and the dataset is not balanced. The performance of various machine learning algorithms is discussed in this paper","downloadable_attachments":[{"id":67642966,"asset_id":49259142,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39122404,"first_name":"IAEME","last_name":"Publication","domain_name":"iaeme","page_name":"publication","display_name":"IAEME Publication","profile_url":"https://iaeme.academia.edu/publication?f_ri=5379","photo":"https://0.academia-photos.com/39122404/12178523/13563629/s65_iaeme.publication.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true},{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest?f_ri=5379","nofollow":true},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm?f_ri=5379","nofollow":false},{"id":3633919,"name":"Naïve Bayes","url":"https://www.academia.edu/Documents/in/Na%C3%AFve_Bayes?f_ri=5379"},{"id":3957281,"name":"Iaeme Ijaret","url":"https://www.academia.edu/Documents/in/Iaeme_Ijaret-1?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43701174" data-work_id="43701174" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43701174/Twitter_Sentiment_Analysis_on_2013_Curriculum_Using_Ensemble_Features_and_K_Nearest_Neighbor">Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43701174" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used including textual features, twitter specific features, lexicon-based features, Parts of Speech (POS) features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43701174" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0bfc7cf7c226acb4eadd89a00bcaeb18" rel="nofollow" data-download="{"attachment_id":64006004,"asset_id":43701174,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64006004/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a><script data-card-contents-for-user="163474776" type="text/json">{"id":163474776,"first_name":"International Journal of Electrical and Computer Engineering","last_name":"(IJECE)","domain_name":"independent","page_name":"JournalIJECE","display_name":"International Journal of Electrical and Computer Engineering (IJECE)","profile_url":"https://independent.academia.edu/JournalIJECE?f_ri=5379","photo":"https://0.academia-photos.com/163474776/123357473/112705609/s65_international_journal_of_electrical_and_computer_engineering._ijece_.jpg"}</script></span></span></li><li class="js-paper-rank-work_43701174 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43701174"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43701174, container: ".js-paper-rank-work_43701174", }); });</script></li><li class="js-percentile-work_43701174 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43701174; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43701174"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43701174 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43701174"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43701174; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43701174]").text(description); $(".js-view-count-work_43701174").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43701174").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43701174"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="922" rel="nofollow" href="https://www.academia.edu/Documents/in/Education">Education</a>, <script data-card-contents-for-ri="922" type="text/json">{"id":922,"name":"Education","url":"https://www.academia.edu/Documents/in/Education?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13873" rel="nofollow" href="https://www.academia.edu/Documents/in/Twitter">Twitter</a><script data-card-contents-for-ri="13873" type="text/json">{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43701174]'), work: {"id":43701174,"title":"Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor","created_at":"2020-07-23T23:56:38.108-07:00","url":"https://www.academia.edu/43701174/Twitter_Sentiment_Analysis_on_2013_Curriculum_Using_Ensemble_Features_and_K_Nearest_Neighbor?f_ri=5379","dom_id":"work_43701174","summary":"2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used including textual features, twitter specific features, lexicon-based features, Parts of Speech (POS) features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.","downloadable_attachments":[{"id":64006004,"asset_id":43701174,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":163474776,"first_name":"International Journal of Electrical and Computer Engineering","last_name":"(IJECE)","domain_name":"independent","page_name":"JournalIJECE","display_name":"International Journal of Electrical and Computer Engineering (IJECE)","profile_url":"https://independent.academia.edu/JournalIJECE?f_ri=5379","photo":"https://0.academia-photos.com/163474776/123357473/112705609/s65_international_journal_of_electrical_and_computer_engineering._ijece_.jpg"}],"research_interests":[{"id":922,"name":"Education","url":"https://www.academia.edu/Documents/in/Education?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification?f_ri=5379"},{"id":862129,"name":"K Nearest Neighbors","url":"https://www.academia.edu/Documents/in/K_Nearest_Neighbors?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12477180" data-work_id="12477180" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12477180/Understanding_the_Postgraduate_Education_Market_for_Better_Marketing_and_Decision_Making_A_Clustering_Analysis">Understanding the Postgraduate Education Market for Better Marketing and Decision Making: A Clustering Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Enhancing the educational corporations is truly challenging mission due to the highly competitive nature of the business. Currently, there is emerging development within organizations to capitalize on their internal resources. This paper... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12477180" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Enhancing the educational corporations is truly challenging mission due to the highly competitive nature of the business. Currently, there is emerging development within organizations to capitalize on their internal resources. This paper is taking data mining approach to strategize marketing for postgraduate studies by means of cluster analysis. The experiments were carried out using Oracle Data Miner tool, results are analyzed and discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12477180" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9875f61cd7999b054c5ea8e293fb1fac" rel="nofollow" data-download="{"attachment_id":37684445,"asset_id":12477180,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37684445/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31321635" href="https://upm.academia.edu/AliMarstawi">Ali Marstawi</a><script data-card-contents-for-user="31321635" type="text/json">{"id":31321635,"first_name":"Ali","last_name":"Marstawi","domain_name":"upm","page_name":"AliMarstawi","display_name":"Ali Marstawi","profile_url":"https://upm.academia.edu/AliMarstawi?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_12477180 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12477180"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12477180, container: ".js-paper-rank-work_12477180", }); });</script></li><li class="js-percentile-work_12477180 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12477180; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_12477180"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_12477180 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="12477180"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12477180; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12477180]").text(description); $(".js-view-count-work_12477180").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12477180").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12477180"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2482" rel="nofollow" href="https://www.academia.edu/Documents/in/Database_Systems">Database Systems</a>, <script data-card-contents-for-ri="2482" type="text/json">{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5187" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Analysis">Statistical Analysis</a>, <script data-card-contents-for-ri="5187" type="text/json">{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12477180]'), work: {"id":12477180,"title":"Understanding the Postgraduate Education Market for Better Marketing and Decision Making: A Clustering Analysis","created_at":"2015-05-19T21:22:53.834-07:00","url":"https://www.academia.edu/12477180/Understanding_the_Postgraduate_Education_Market_for_Better_Marketing_and_Decision_Making_A_Clustering_Analysis?f_ri=5379","dom_id":"work_12477180","summary":"Enhancing the educational corporations is truly challenging mission due to the highly competitive nature of the business. Currently, there is emerging development within organizations to capitalize on their internal resources. This paper is taking data mining approach to strategize marketing for postgraduate studies by means of cluster analysis. The experiments were carried out using Oracle Data Miner tool, results are analyzed and discussed.","downloadable_attachments":[{"id":37684445,"asset_id":12477180,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31321635,"first_name":"Ali","last_name":"Marstawi","domain_name":"upm","page_name":"AliMarstawi","display_name":"Ali Marstawi","profile_url":"https://upm.academia.edu/AliMarstawi?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=5379","nofollow":true},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379"},{"id":6869,"name":"Qur'anic Studies","url":"https://www.academia.edu/Documents/in/Quranic_Studies?f_ri=5379"},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=5379"},{"id":1223708,"name":"Qur'anic and Hadith sciences","url":"https://www.academia.edu/Documents/in/Quranic_and_Hadith_sciences?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9050591 coauthored" data-work_id="9050591" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9050591/Social_media_analysis_for_product_safety_using_text_mining_and_sentiment_analysis">Social media analysis for product safety using text mining and sentiment analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9050591" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9050591" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4c4e2004260eff45078398db6614d1b3" rel="nofollow" data-download="{"attachment_id":35382972,"asset_id":9050591,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35382972/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2774789" href="https://brad.academia.edu/HarunaIsah">Haruna Isah</a><script data-card-contents-for-user="2774789" type="text/json">{"id":2774789,"first_name":"Haruna","last_name":"Isah","domain_name":"brad","page_name":"HarunaIsah","display_name":"Haruna Isah","profile_url":"https://brad.academia.edu/HarunaIsah?f_ri=5379","photo":"https://0.academia-photos.com/2774789/904238/11414416/s65_haruna.isah.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-9050591">+1</span><div class="hidden js-additional-users-9050591"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://bradford.academia.edu/DanielNeagu">Daniel Neagu</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-9050591'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-9050591').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_9050591 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9050591"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9050591, container: ".js-paper-rank-work_9050591", }); });</script></li><li class="js-percentile-work_9050591 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 9050591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_9050591"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); 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User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.","downloadable_attachments":[{"id":35382972,"asset_id":9050591,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2774789,"first_name":"Haruna","last_name":"Isah","domain_name":"brad","page_name":"HarunaIsah","display_name":"Haruna Isah","profile_url":"https://brad.academia.edu/HarunaIsah?f_ri=5379","photo":"https://0.academia-photos.com/2774789/904238/11414416/s65_haruna.isah.jpg"},{"id":144577,"first_name":"Daniel","last_name":"Neagu","domain_name":"bradford","page_name":"DanielNeagu","display_name":"Daniel Neagu","profile_url":"https://bradford.academia.edu/DanielNeagu?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2553,"name":"Social Networking","url":"https://www.academia.edu/Documents/in/Social_Networking?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379"},{"id":14494,"name":"Opinion Mining (Data Mining)","url":"https://www.academia.edu/Documents/in/Opinion_Mining_Data_Mining_?f_ri=5379"},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4126451" data-work_id="4126451" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4126451/ELS_a_word_level_method_for_entity_level_sentiment_analysis">ELS: a word-level method for entity-level sentiment analysis</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4126451" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="70032c826639617e432f1e3271344699" rel="nofollow" data-download="{"attachment_id":50021431,"asset_id":4126451,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50021431/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1913727" href="https://uni-saarland.academia.edu/AngelikiLazaridou">Angeliki Lazaridou</a><script data-card-contents-for-user="1913727" type="text/json">{"id":1913727,"first_name":"Angeliki","last_name":"Lazaridou","domain_name":"uni-saarland","page_name":"AngelikiLazaridou","display_name":"Angeliki Lazaridou","profile_url":"https://uni-saarland.academia.edu/AngelikiLazaridou?f_ri=5379","photo":"https://0.academia-photos.com/1913727/646756/801789/s65_angeliki.lazaridou.jpg"}</script></span></span></li><li class="js-paper-rank-work_4126451 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4126451"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4126451, container: ".js-paper-rank-work_4126451", }); 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$(".js-view-count[data-work-id=4126451]").text(description); $(".js-view-count-work_4126451").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4126451").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4126451"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="201685" rel="nofollow" href="https://www.academia.edu/Documents/in/Opinion_Mining">Opinion Mining</a>, <script data-card-contents-for-ri="201685" type="text/json">{"id":201685,"name":"Opinion Mining","url":"https://www.academia.edu/Documents/in/Opinion_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="688444" rel="nofollow" href="https://www.academia.edu/Documents/in/Conditional_Random_Field">Conditional Random Field</a>, <script data-card-contents-for-ri="688444" type="text/json">{"id":688444,"name":"Conditional Random Field","url":"https://www.academia.edu/Documents/in/Conditional_Random_Field?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="820369" rel="nofollow" href="https://www.academia.edu/Documents/in/Pattern_Discovery">Pattern Discovery</a><script data-card-contents-for-ri="820369" type="text/json">{"id":820369,"name":"Pattern Discovery","url":"https://www.academia.edu/Documents/in/Pattern_Discovery?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4126451]'), work: {"id":4126451,"title":"ELS: a word-level method for entity-level sentiment analysis","created_at":"2013-07-29T00:45:30.118-07:00","url":"https://www.academia.edu/4126451/ELS_a_word_level_method_for_entity_level_sentiment_analysis?f_ri=5379","dom_id":"work_4126451","summary":null,"downloadable_attachments":[{"id":50021431,"asset_id":4126451,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1913727,"first_name":"Angeliki","last_name":"Lazaridou","domain_name":"uni-saarland","page_name":"AngelikiLazaridou","display_name":"Angeliki Lazaridou","profile_url":"https://uni-saarland.academia.edu/AngelikiLazaridou?f_ri=5379","photo":"https://0.academia-photos.com/1913727/646756/801789/s65_angeliki.lazaridou.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":201685,"name":"Opinion Mining","url":"https://www.academia.edu/Documents/in/Opinion_Mining?f_ri=5379","nofollow":true},{"id":688444,"name":"Conditional Random Field","url":"https://www.academia.edu/Documents/in/Conditional_Random_Field?f_ri=5379","nofollow":true},{"id":820369,"name":"Pattern Discovery","url":"https://www.academia.edu/Documents/in/Pattern_Discovery?f_ri=5379","nofollow":true},{"id":1260012,"name":"Bag of Words","url":"https://www.academia.edu/Documents/in/Bag_of_Words?f_ri=5379"},{"id":2467717,"name":"Sentiment Classification","url":"https://www.academia.edu/Documents/in/Sentiment_Classification?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35770952" data-work_id="35770952" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35770952/Tunnistaako_kone_tunteesi_S%C3%A4vyanalyysi_sosiaalisen_median_sis%C3%A4lt%C3%B6jen_tunnistamisessa">Tunnistaako kone tunteesi? Sävyanalyysi sosiaalisen median sisältöjen tunnistamisessa.</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Tietokoneiden laskentatehon kasvu on tehnyt mahdolliseksi asioiden, tapahtumien ja ilmiöiden synty- ja leviämismekanismien analysoinnin aiempaa tarkemmin. Optimistisimmat ovat arvioineet, että tietoteknologia on synnyttämässä uutta... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35770952" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Tietokoneiden laskentatehon kasvu on tehnyt mahdolliseksi asioiden, tapahtumien ja ilmiöiden synty- ja leviämismekanismien analysoinnin aiempaa tarkemmin. Optimistisimmat ovat arvioineet, että tietoteknologia on synnyttämässä uutta tiedettä – sosiaalifysiikkaa (social physics), joka antaa uusia mahdollisuuksia ihmisten käyttäytymisen ja ilmiöiden leviämisen tutkimiseen (Pentland 2014). Uutta tutkimusta ja uusia analysointimenetelmiä tarvitaan, sillä sosiaalinen media on tehnyt monesta yksityisestä ja aiemmin piiloon jääneestä asiasta julkista. Ei olekaan yllättävää, että sosiaalinen analytiikka ja erityisesti tunteita ja mielipiteitä ilmentävien sisältöjen automaattinen tunnistaminen on tätä nykyä yksi nopeimmin kasvavista tietoteknologian sovellusalueista (Gartner 2014). Sosiaalisella analytiikalla viitataan väljästi menetelmiin, työkaluihin ja toimintatapoihin, joilla organisaatiot pyrkivät hyödyntämään virtuaalisista sosiaalisista suhteista kertyvää tietoa oman toimintansa kehittämisessä. <br /><br />Sosiaalista analytiikkaa on muun muassa se, kun yritys monitoroi ja analysoi yhteisösivustojen keskusteluja, jotta se voi kohdentaa mainontaansa paremmin, tunnistaa piilevää kysyntää tai oppia asiakkaidensa tuote- ja palvelukokemuksista. Yritysten ohella sosiaalinen analytiikka tarjoaa myös julkishallinnolle lukemattomia käyttökohteita. Esimerkiksi kunnalle on arvokasta tietää, millaisessa sävyssä sen tekemisistä ja palveluista sosiaalisessa mediassa keskustellaan. Kuntalaisten mielipiteiden parempi ymmärtäminen antaa mahdollisuuden käynnistää toimenpiteitä ja tehdä ratkaisuja, jotka vastaavat kuntalaisten odotuksia. Erityisesti nuorten kohdalla sosiaalisen analytiikan avulla voidaan saada aikaan hyviä tuloksia, jos nuorille syntyy tunne siitä, että heidän tarpeensa on huomattu. Muodollisilla palautejärjestelmillä on tulevaisuudessakin paikkansa, mutta demokratia voi sitä paremmin, mitä luontevammin hallintokoneisto ja kansalaiset toisensa kohtaavat. Sosiaalinen analytiikka ei yksinään riitä ratkaisuksi, mutta se saattaa hyvinkin olla keskeinen osa sitä.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35770952" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0f224146127d51695d6005b7c248b17a" rel="nofollow" data-download="{"attachment_id":55647630,"asset_id":35770952,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55647630/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3316399" href="https://turkuamk.academia.edu/HarriJalonen">Harri Jalonen</a><script data-card-contents-for-user="3316399" type="text/json">{"id":3316399,"first_name":"Harri","last_name":"Jalonen","domain_name":"turkuamk","page_name":"HarriJalonen","display_name":"Harri Jalonen","profile_url":"https://turkuamk.academia.edu/HarriJalonen?f_ri=5379","photo":"https://0.academia-photos.com/3316399/1107070/6895494/s65_harri.jalonen.jpg"}</script></span></span></li><li class="js-paper-rank-work_35770952 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35770952"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35770952, container: ".js-paper-rank-work_35770952", }); 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$(".js-view-count[data-work-id=35770952]").text(description); $(".js-view-count-work_35770952").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35770952").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35770952"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13873" rel="nofollow" href="https://www.academia.edu/Documents/in/Twitter">Twitter</a>, <script data-card-contents-for-ri="13873" type="text/json">{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="21498" rel="nofollow" href="https://www.academia.edu/Documents/in/Affect_Emotion">Affect/Emotion</a><script data-card-contents-for-ri="21498" type="text/json">{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35770952]'), work: {"id":35770952,"title":"Tunnistaako kone tunteesi? Sävyanalyysi sosiaalisen median sisältöjen tunnistamisessa.","created_at":"2018-01-27T07:37:57.151-08:00","url":"https://www.academia.edu/35770952/Tunnistaako_kone_tunteesi_S%C3%A4vyanalyysi_sosiaalisen_median_sis%C3%A4lt%C3%B6jen_tunnistamisessa?f_ri=5379","dom_id":"work_35770952","summary":"Tietokoneiden laskentatehon kasvu on tehnyt mahdolliseksi asioiden, tapahtumien ja ilmiöiden synty- ja leviämismekanismien analysoinnin aiempaa tarkemmin. Optimistisimmat ovat arvioineet, että tietoteknologia on synnyttämässä uutta tiedettä – sosiaalifysiikkaa (social physics), joka antaa uusia mahdollisuuksia ihmisten käyttäytymisen ja ilmiöiden leviämisen tutkimiseen (Pentland 2014). Uutta tutkimusta ja uusia analysointimenetelmiä tarvitaan, sillä sosiaalinen media on tehnyt monesta yksityisestä ja aiemmin piiloon jääneestä asiasta julkista. Ei olekaan yllättävää, että sosiaalinen analytiikka ja erityisesti tunteita ja mielipiteitä ilmentävien sisältöjen automaattinen tunnistaminen on tätä nykyä yksi nopeimmin kasvavista tietoteknologian sovellusalueista (Gartner 2014). Sosiaalisella analytiikalla viitataan väljästi menetelmiin, työkaluihin ja toimintatapoihin, joilla organisaatiot pyrkivät hyödyntämään virtuaalisista sosiaalisista suhteista kertyvää tietoa oman toimintansa kehittämisessä. \n\nSosiaalista analytiikkaa on muun muassa se, kun yritys monitoroi ja analysoi yhteisösivustojen keskusteluja, jotta se voi kohdentaa mainontaansa paremmin, tunnistaa piilevää kysyntää tai oppia asiakkaidensa tuote- ja palvelukokemuksista. Yritysten ohella sosiaalinen analytiikka tarjoaa myös julkishallinnolle lukemattomia käyttökohteita. Esimerkiksi kunnalle on arvokasta tietää, millaisessa sävyssä sen tekemisistä ja palveluista sosiaalisessa mediassa keskustellaan. Kuntalaisten mielipiteiden parempi ymmärtäminen antaa mahdollisuuden käynnistää toimenpiteitä ja tehdä ratkaisuja, jotka vastaavat kuntalaisten odotuksia. Erityisesti nuorten kohdalla sosiaalisen analytiikan avulla voidaan saada aikaan hyviä tuloksia, jos nuorille syntyy tunne siitä, että heidän tarpeensa on huomattu. Muodollisilla palautejärjestelmillä on tulevaisuudessakin paikkansa, mutta demokratia voi sitä paremmin, mitä luontevammin hallintokoneisto ja kansalaiset toisensa kohtaavat. Sosiaalinen analytiikka ei yksinään riitä ratkaisuksi, mutta se saattaa hyvinkin olla keskeinen osa sitä.\n","downloadable_attachments":[{"id":55647630,"asset_id":35770952,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3316399,"first_name":"Harri","last_name":"Jalonen","domain_name":"turkuamk","page_name":"HarriJalonen","display_name":"Harri Jalonen","profile_url":"https://turkuamk.academia.edu/HarriJalonen?f_ri=5379","photo":"https://0.academia-photos.com/3316399/1107070/6895494/s65_harri.jalonen.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379","nofollow":true},{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true},{"id":174006,"name":"Twitter as a News Source, Twitter as Medium and Message","url":"https://www.academia.edu/Documents/in/Twitter_as_a_News_Source_Twitter_as_Medium_and_Message?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43738949" data-work_id="43738949" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43738949/Feature_level_Rating_System_using_Customer_Reviews_and_Review_Votes">Feature-level Rating System using Customer Reviews and Review Votes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43738949" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the customer's perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer's perspective) and in making the personalized buying decisions (from the buyer's perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43738949" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c014a79306e1d8f9b3d8f1454550cf02" rel="nofollow" data-download="{"attachment_id":80047625,"asset_id":43738949,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80047625/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="117702953" href="https://iiitd.academia.edu/KoteswarRaoJerripothula">Koteswar Rao Jerripothula</a><script data-card-contents-for-user="117702953" type="text/json">{"id":117702953,"first_name":"Koteswar Rao","last_name":"Jerripothula","domain_name":"iiitd","page_name":"KoteswarRaoJerripothula","display_name":"Koteswar Rao Jerripothula","profile_url":"https://iiitd.academia.edu/KoteswarRaoJerripothula?f_ri=5379","photo":"https://0.academia-photos.com/117702953/28811899/26901072/s65_koteswar_rao.jerripothula.jpg"}</script></span></span></li><li class="js-paper-rank-work_43738949 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43738949"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43738949, container: ".js-paper-rank-work_43738949", }); });</script></li><li class="js-percentile-work_43738949 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43738949; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43738949"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43738949 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43738949"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43738949; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43738949]").text(description); $(".js-view-count-work_43738949").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43738949").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43738949"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2900" rel="nofollow" href="https://www.academia.edu/Documents/in/Recommender_Systems">Recommender Systems</a>, <script data-card-contents-for-ri="2900" type="text/json">{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="18390" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_Phones">Mobile Phones</a>, <script data-card-contents-for-ri="18390" type="text/json">{"id":18390,"name":"Mobile Phones","url":"https://www.academia.edu/Documents/in/Mobile_Phones?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="208759" rel="nofollow" href="https://www.academia.edu/Documents/in/Customer_experience">Customer experience</a><script data-card-contents-for-ri="208759" type="text/json">{"id":208759,"name":"Customer experience","url":"https://www.academia.edu/Documents/in/Customer_experience?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43738949]'), work: {"id":43738949,"title":"Feature-level Rating System using Customer Reviews and Review Votes","created_at":"2020-07-29T09:11:06.052-07:00","url":"https://www.academia.edu/43738949/Feature_level_Rating_System_using_Customer_Reviews_and_Review_Votes?f_ri=5379","dom_id":"work_43738949","summary":"This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the customer's perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer's perspective) and in making the personalized buying decisions (from the buyer's perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.","downloadable_attachments":[{"id":80047625,"asset_id":43738949,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":117702953,"first_name":"Koteswar Rao","last_name":"Jerripothula","domain_name":"iiitd","page_name":"KoteswarRaoJerripothula","display_name":"Koteswar Rao Jerripothula","profile_url":"https://iiitd.academia.edu/KoteswarRaoJerripothula?f_ri=5379","photo":"https://0.academia-photos.com/117702953/28811899/26901072/s65_koteswar_rao.jerripothula.jpg"}],"research_interests":[{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":18390,"name":"Mobile Phones","url":"https://www.academia.edu/Documents/in/Mobile_Phones?f_ri=5379","nofollow":true},{"id":208759,"name":"Customer experience","url":"https://www.academia.edu/Documents/in/Customer_experience?f_ri=5379","nofollow":true},{"id":537605,"name":"Natural Language Processing(NLP)","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing_NLP_?f_ri=5379"},{"id":544021,"name":"Business Administration and E-Commerce","url":"https://www.academia.edu/Documents/in/Business_Administration_and_E-Commerce?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40103487" data-work_id="40103487" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40103487/Love_and_Loss_in_War_and_Peace_">Love and Loss in "War and Peace"</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The tragic tale of Prince Andrei and Natasha Rostov that is the centerpiece of Tolstoy’s masterpiece is a brilliant depiction of the fallibility of life, sublimity of love and serenity in death. Andrei was a middle-aged widower and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40103487" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The tragic tale of Prince Andrei and Natasha Rostov that is the centerpiece of Tolstoy’s masterpiece is a brilliant depiction of the fallibility of life, sublimity of love and serenity in death. Andrei was a middle-aged widower and Natasha was still in her teens when love happened to them. While Natasha’s parents welcomed their romance, Andrei’s father was scornful about the match of that ‘chit of a girl’ from the family of no fortune or rank of consequence. However, he gives in subject to the condition that Andrei should put it off for a year and stay abroad during that period. “And then if your love or passion or obduracy – whatever you choose – is still as great, marry!” says the old man.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40103487" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4e13f7ad9afb946a5555f00e3a0586b7" rel="nofollow" data-download="{"attachment_id":64181471,"asset_id":40103487,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64181471/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42509944" href="https://independent.academia.edu/BulusuSMurthy">BS Murthy</a><script data-card-contents-for-user="42509944" type="text/json">{"id":42509944,"first_name":"BS","last_name":"Murthy","domain_name":"independent","page_name":"BulusuSMurthy","display_name":"BS Murthy","profile_url":"https://independent.academia.edu/BulusuSMurthy?f_ri=5379","photo":"https://0.academia-photos.com/42509944/11445231/12765992/s65_bulusu_s.murthy.jpg"}</script></span></span></li><li class="js-paper-rank-work_40103487 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40103487"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40103487, container: ".js-paper-rank-work_40103487", }); });</script></li><li class="js-percentile-work_40103487 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40103487; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40103487"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40103487 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40103487"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40103487; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40103487]").text(description); $(".js-view-count-work_40103487").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40103487").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40103487"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="667" rel="nofollow" href="https://www.academia.edu/Documents/in/Russian_Literature">Russian Literature</a>, <script data-card-contents-for-ri="667" type="text/json">{"id":667,"name":"Russian Literature","url":"https://www.academia.edu/Documents/in/Russian_Literature?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2418" rel="nofollow" href="https://www.academia.edu/Documents/in/Literature">Literature</a>, <script data-card-contents-for-ri="2418" type="text/json">{"id":2418,"name":"Literature","url":"https://www.academia.edu/Documents/in/Literature?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10187" rel="nofollow" href="https://www.academia.edu/Documents/in/Love">Love</a><script data-card-contents-for-ri="10187" type="text/json">{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40103487]'), work: {"id":40103487,"title":"Love and Loss in \"War and Peace\"","created_at":"2019-08-16T20:42:56.212-07:00","url":"https://www.academia.edu/40103487/Love_and_Loss_in_War_and_Peace_?f_ri=5379","dom_id":"work_40103487","summary":"The tragic tale of Prince Andrei and Natasha Rostov that is the centerpiece of Tolstoy’s masterpiece is a brilliant depiction of the fallibility of life, sublimity of love and serenity in death. Andrei was a middle-aged widower and Natasha was still in her teens when love happened to them. While Natasha’s parents welcomed their romance, Andrei’s father was scornful about the match of that ‘chit of a girl’ from the family of no fortune or rank of consequence. However, he gives in subject to the condition that Andrei should put it off for a year and stay abroad during that period. “And then if your love or passion or obduracy – whatever you choose – is still as great, marry!” says the old man. ","downloadable_attachments":[{"id":64181471,"asset_id":40103487,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42509944,"first_name":"BS","last_name":"Murthy","domain_name":"independent","page_name":"BulusuSMurthy","display_name":"BS Murthy","profile_url":"https://independent.academia.edu/BulusuSMurthy?f_ri=5379","photo":"https://0.academia-photos.com/42509944/11445231/12765992/s65_bulusu_s.murthy.jpg"}],"research_interests":[{"id":667,"name":"Russian Literature","url":"https://www.academia.edu/Documents/in/Russian_Literature?f_ri=5379","nofollow":true},{"id":2418,"name":"Literature","url":"https://www.academia.edu/Documents/in/Literature?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love?f_ri=5379","nofollow":true},{"id":18900,"name":"Philosophy of Love","url":"https://www.academia.edu/Documents/in/Philosophy_of_Love?f_ri=5379"},{"id":19977,"name":"War and society","url":"https://www.academia.edu/Documents/in/War_and_society?f_ri=5379"},{"id":27208,"name":"Social Studies","url":"https://www.academia.edu/Documents/in/Social_Studies?f_ri=5379"},{"id":32865,"name":"War and Peace","url":"https://www.academia.edu/Documents/in/War_and_Peace?f_ri=5379"},{"id":37454,"name":"Tolstoy","url":"https://www.academia.edu/Documents/in/Tolstoy?f_ri=5379"},{"id":42162,"name":"Emotions","url":"https://www.academia.edu/Documents/in/Emotions?f_ri=5379"},{"id":44675,"name":"Fiction","url":"https://www.academia.edu/Documents/in/Fiction?f_ri=5379"},{"id":49779,"name":"Theories of Love","url":"https://www.academia.edu/Documents/in/Theories_of_Love?f_ri=5379"},{"id":110416,"name":"General Studies","url":"https://www.academia.edu/Documents/in/General_Studies?f_ri=5379"},{"id":137762,"name":"Sentimentalism","url":"https://www.academia.edu/Documents/in/Sentimentalism?f_ri=5379"},{"id":1821913,"name":"Longing","url":"https://www.academia.edu/Documents/in/Longing?f_ri=5379"},{"id":1891227,"name":"Loss and Longing","url":"https://www.academia.edu/Documents/in/Loss_and_Longing?f_ri=5379"},{"id":2000522,"name":"Longing in Literature","url":"https://www.academia.edu/Documents/in/Longing_in_Literature?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_27376691" data-work_id="27376691" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/27376691/Predicting_Star_Ratings_based_on_Annotated_Reviews_of_Mobile_Apps">Predicting Star Ratings based on Annotated Reviews of Mobile Apps</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_27376691" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play Store in a related work. Fine-granular opinions and the classification of their sentiment orientation were already available. The models build upon them to make predictions based on their polarity. Predicting star ratings is of importance to the sentiment analysis community because it can better be understood how customers subjectively rate products. Rating them consistently with corresponding written reviews, however, remains a difficult task for automated predictors. This paper sheds new light in that direction.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27376691" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="eedda5efd64c6f4ddc99a566ecca124d" rel="nofollow" data-download="{"attachment_id":47630545,"asset_id":27376691,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47630545/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2510674" href="https://hwr-berlin.academia.edu/DagmarMonett">Dagmar Monett</a><script data-card-contents-for-user="2510674" type="text/json">{"id":2510674,"first_name":"Dagmar","last_name":"Monett","domain_name":"hwr-berlin","page_name":"DagmarMonett","display_name":"Dagmar Monett","profile_url":"https://hwr-berlin.academia.edu/DagmarMonett?f_ri=5379","photo":"https://0.academia-photos.com/2510674/15210684/28509197/s65_dagmar.monett.jpg"}</script></span></span></li><li class="js-paper-rank-work_27376691 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="27376691"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 27376691, container: ".js-paper-rank-work_27376691", }); });</script></li><li class="js-percentile-work_27376691 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 27376691; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_27376691"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_27376691 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="27376691"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 27376691; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=27376691]").text(description); $(".js-view-count-work_27376691").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_27376691").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="27376691"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="65030" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_apps">Mobile apps</a>, <script data-card-contents-for-ri="65030" type="text/json">{"id":65030,"name":"Mobile apps","url":"https://www.academia.edu/Documents/in/Mobile_apps?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="753695" rel="nofollow" href="https://www.academia.edu/Documents/in/Opinion_Mining_and_Polarity_Detection">Opinion Mining and Polarity Detection</a>, <script data-card-contents-for-ri="753695" type="text/json">{"id":753695,"name":"Opinion Mining and Polarity Detection","url":"https://www.academia.edu/Documents/in/Opinion_Mining_and_Polarity_Detection?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1426974" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis_and_Opinion_Mining">Sentiment Analysis and Opinion Mining</a><script data-card-contents-for-ri="1426974" type="text/json">{"id":1426974,"name":"Sentiment Analysis and Opinion Mining","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis_and_Opinion_Mining?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=27376691]'), work: {"id":27376691,"title":"Predicting Star Ratings based on Annotated Reviews of Mobile Apps","created_at":"2016-07-29T13:42:08.757-07:00","url":"https://www.academia.edu/27376691/Predicting_Star_Ratings_based_on_Annotated_Reviews_of_Mobile_Apps?f_ri=5379","dom_id":"work_27376691","summary":"This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play Store in a related work. Fine-granular opinions and the classification of their sentiment orientation were already available. The models build upon them to make predictions based on their polarity. Predicting star ratings is of importance to the sentiment analysis community because it can better be understood how customers subjectively rate products. Rating them consistently with corresponding written reviews, however, remains a difficult task for automated predictors. This paper sheds new light in that direction.","downloadable_attachments":[{"id":47630545,"asset_id":27376691,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2510674,"first_name":"Dagmar","last_name":"Monett","domain_name":"hwr-berlin","page_name":"DagmarMonett","display_name":"Dagmar Monett","profile_url":"https://hwr-berlin.academia.edu/DagmarMonett?f_ri=5379","photo":"https://0.academia-photos.com/2510674/15210684/28509197/s65_dagmar.monett.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":65030,"name":"Mobile apps","url":"https://www.academia.edu/Documents/in/Mobile_apps?f_ri=5379","nofollow":true},{"id":753695,"name":"Opinion Mining and Polarity Detection","url":"https://www.academia.edu/Documents/in/Opinion_Mining_and_Polarity_Detection?f_ri=5379","nofollow":true},{"id":1426974,"name":"Sentiment Analysis and Opinion Mining","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis_and_Opinion_Mining?f_ri=5379","nofollow":true},{"id":2504737,"name":"review rating prediction","url":"https://www.academia.edu/Documents/in/review_rating_prediction?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68590323" data-work_id="68590323" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68590323/Understanding_WorldEnvironmentDay_User_Opinions_in_Twitter_A_Topic_Based_Sentiment_Analysis_Approach">Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68590323" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most conc...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68590323" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="053b489b36a3cf2e7585a96b2837c6af" rel="nofollow" data-download="{"attachment_id":79016947,"asset_id":68590323,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79016947/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="92314712" href="https://independent.academia.edu/C%C3%A9sar%C3%81lvarezAlonso">César Álvarez Alonso</a><script data-card-contents-for-user="92314712" type="text/json">{"id":92314712,"first_name":"César","last_name":"Álvarez Alonso","domain_name":"independent","page_name":"CésarÁlvarezAlonso","display_name":"César Álvarez Alonso","profile_url":"https://independent.academia.edu/C%C3%A9sar%C3%81lvarezAlonso?f_ri=5379","photo":"https://0.academia-photos.com/92314712/110852552/100103059/s65_c_sar._lvarez_alonso.jpeg"}</script></span></span></li><li class="js-paper-rank-work_68590323 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68590323"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68590323, container: ".js-paper-rank-work_68590323", }); });</script></li><li class="js-percentile-work_68590323 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 68590323; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_68590323"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_68590323 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="68590323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 68590323; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=68590323]").text(description); $(".js-view-count-work_68590323").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_68590323").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="68590323"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="26" rel="nofollow" href="https://www.academia.edu/Documents/in/Business">Business</a>, <script data-card-contents-for-ri="26" type="text/json">{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4524" rel="nofollow" href="https://www.academia.edu/Documents/in/Sustainable_Development">Sustainable Development</a>, <script data-card-contents-for-ri="4524" type="text/json">{"id":4524,"name":"Sustainable Development","url":"https://www.academia.edu/Documents/in/Sustainable_Development?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=68590323]'), work: {"id":68590323,"title":"Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach","created_at":"2022-01-18T01:51:02.481-08:00","url":"https://www.academia.edu/68590323/Understanding_WorldEnvironmentDay_User_Opinions_in_Twitter_A_Topic_Based_Sentiment_Analysis_Approach?f_ri=5379","dom_id":"work_68590323","summary":"The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most conc...","downloadable_attachments":[{"id":79016947,"asset_id":68590323,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":92314712,"first_name":"César","last_name":"Álvarez Alonso","domain_name":"independent","page_name":"CésarÁlvarezAlonso","display_name":"César Álvarez Alonso","profile_url":"https://independent.academia.edu/C%C3%A9sar%C3%81lvarezAlonso?f_ri=5379","photo":"https://0.academia-photos.com/92314712/110852552/100103059/s65_c_sar._lvarez_alonso.jpeg"}],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":4524,"name":"Sustainable Development","url":"https://www.academia.edu/Documents/in/Sustainable_Development?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=5379"},{"id":28032,"name":"NVivo","url":"https://www.academia.edu/Documents/in/NVivo?f_ri=5379"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=5379"},{"id":85038,"name":"Environmental public health","url":"https://www.academia.edu/Documents/in/Environmental_public_health?f_ri=5379"},{"id":165615,"name":"Textual analysis","url":"https://www.academia.edu/Documents/in/Textual_analysis?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68241651" data-work_id="68241651" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68241651/Sentiment_Analysis_of_Informal_Persian_Texts_Using_Embedding_Informal_words_and_Attention_Based_LSTM_Network">Sentiment Analysis of Informal Persian Texts Using Embedding Informal words and Attention-Based LSTM Network</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68241651" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which refers to the determination of the sentiment of texts, has been proposed as an intelligent solution. From a methodological point of view, the recent combination of words embedding and deep neural networks (DNNs) has become an effective approach for sentiment analysis. In Persian studies, formal corpuses such as Wikipedia dumps have been used for word embedding. The fundamental difference between formal and informal texts means that the vectors derived from formal texts in informal contexts such as social networks do not result in desirable accuracy. To overcome this drawback, in this paper, we provide a large integrated text corpus of several different sources of informal comments and we also utilize the Fasttext as the word embedding algorithm. In this research, we use Attention-based LSTM, which has been shown to perform more effectively compared to the similar methods in sentiment analysis for the English language. The proposed method is evaluated on the two Persian "Taaghche" and "Filimo" datasets collected in this paper. The experiments on the two Persian datasets prove that utilizing informal vectors in sentiment analysis and applying the attention model improves the prediction accuracy of the DNN in the sentiment analysis of Persian texts.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68241651" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e2d3973eaabbea3e58f3bfe710dfd052" rel="nofollow" data-download="{"attachment_id":78787895,"asset_id":68241651,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78787895/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="212395741" href="https://qiau.academia.edu/MohammadFarahani">Mohammad Farahani</a><script data-card-contents-for-user="212395741" type="text/json">{"id":212395741,"first_name":"Mohammad","last_name":"Farahani","domain_name":"qiau","page_name":"MohammadFarahani","display_name":"Mohammad Farahani","profile_url":"https://qiau.academia.edu/MohammadFarahani?f_ri=5379","photo":"https://0.academia-photos.com/212395741/71519276/59966265/s65_mohammad.farahani.jpg"}</script></span></span></li><li class="js-paper-rank-work_68241651 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68241651"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68241651, container: ".js-paper-rank-work_68241651", }); });</script></li><li class="js-percentile-work_68241651 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 68241651; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_68241651"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_68241651 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="68241651"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 68241651; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=68241651]").text(description); $(".js-view-count-work_68241651").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_68241651").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="68241651"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1819738" rel="nofollow" href="https://www.academia.edu/Documents/in/Word_Embedding">Word Embedding</a><script data-card-contents-for-ri="1819738" type="text/json">{"id":1819738,"name":"Word Embedding","url":"https://www.academia.edu/Documents/in/Word_Embedding?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=68241651]'), work: {"id":68241651,"title":"Sentiment Analysis of Informal Persian Texts Using Embedding Informal words and Attention-Based LSTM Network","created_at":"2022-01-15T02:24:53.230-08:00","url":"https://www.academia.edu/68241651/Sentiment_Analysis_of_Informal_Persian_Texts_Using_Embedding_Informal_words_and_Attention_Based_LSTM_Network?f_ri=5379","dom_id":"work_68241651","summary":"The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which refers to the determination of the sentiment of texts, has been proposed as an intelligent solution. From a methodological point of view, the recent combination of words embedding and deep neural networks (DNNs) has become an effective approach for sentiment analysis. In Persian studies, formal corpuses such as Wikipedia dumps have been used for word embedding. The fundamental difference between formal and informal texts means that the vectors derived from formal texts in informal contexts such as social networks do not result in desirable accuracy. To overcome this drawback, in this paper, we provide a large integrated text corpus of several different sources of informal comments and we also utilize the Fasttext as the word embedding algorithm. In this research, we use Attention-based LSTM, which has been shown to perform more effectively compared to the similar methods in sentiment analysis for the English language. The proposed method is evaluated on the two Persian \"Taaghche\" and \"Filimo\" datasets collected in this paper. The experiments on the two Persian datasets prove that utilizing informal vectors in sentiment analysis and applying the attention model improves the prediction accuracy of the DNN in the sentiment analysis of Persian texts.","downloadable_attachments":[{"id":78787895,"asset_id":68241651,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":212395741,"first_name":"Mohammad","last_name":"Farahani","domain_name":"qiau","page_name":"MohammadFarahani","display_name":"Mohammad Farahani","profile_url":"https://qiau.academia.edu/MohammadFarahani?f_ri=5379","photo":"https://0.academia-photos.com/212395741/71519276/59966265/s65_mohammad.farahani.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":1819738,"name":"Word Embedding","url":"https://www.academia.edu/Documents/in/Word_Embedding?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14209333" data-work_id="14209333" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14209333/Sentiment_Analysis_of_Features_in_Review_Mining">Sentiment Analysis of Features in Review Mining</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Apparently, Amazon is the leader in online shopping. Huge customer database, Enormous options to buy and trust makes amazon a better place to shop. Customer reviews are the one of the most influencing factors which decides the future of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14209333" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Apparently, Amazon is the leader in online shopping. Huge customer database, Enormous options to buy and trust makes amazon a better place to shop. Customer reviews are the one of the most influencing factors which decides the future of the product, whether the customer is going to buy that product or not. The objective of writing this paper is to provide an extra lineament for the product review system of amazon. In this paper we come up with the idea of sentiment analysis of features in review mining and consolidate the précis of review. We suggest the idea of extracting the polarity of the given set of features from the reviews so that we can provide a better summarized reviews of the product to the customer in term of features. For this purpose we are using different sentimental analysis algorithms for comparing the outputs. Our system automatically separates the review data into objective and subjective information. And also apply POS Tagging to refine our subjective data. We have used this refined subjective data as an input in our experiment. For experimental purpose we have trained subjective review data with SVM Classifier in WEKA. We also used stop-word list to reduce the noise while classifying the review strings. In Evaluation we tested the test data with different tunned parameters like folds and percentage split and also did the cross validation for test data set. Evaluation results shows 95% of data were classified correctly in all test data set. Based on our trained data set new review feature values will be predicted. Finally our system calculates features score for each product. The aim of this paper is to inform future buyers of the product what aspects of the product are good and bad according to the previous buyers. This is indeed very valuable information.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14209333" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e44879aa99841aa1200d6a3642f245ae" rel="nofollow" data-download="{"attachment_id":38237742,"asset_id":14209333,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38237742/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33195352" href="https://independent.academia.edu/ShivamAgrawal10">Shivam Agrawal</a><script data-card-contents-for-user="33195352" type="text/json">{"id":33195352,"first_name":"Shivam","last_name":"Agrawal","domain_name":"independent","page_name":"ShivamAgrawal10","display_name":"Shivam Agrawal","profile_url":"https://independent.academia.edu/ShivamAgrawal10?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_14209333 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14209333"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14209333, container: ".js-paper-rank-work_14209333", }); });</script></li><li class="js-percentile-work_14209333 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 14209333; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_14209333"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_14209333 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="14209333"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 14209333; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=14209333]").text(description); $(".js-view-count-work_14209333").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14209333").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14209333"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13690" rel="nofollow" href="https://www.academia.edu/Documents/in/Ecommerce">Ecommerce</a>, <script data-card-contents-for-ri="13690" type="text/json">{"id":13690,"name":"Ecommerce","url":"https://www.academia.edu/Documents/in/Ecommerce?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="174320" rel="nofollow" href="https://www.academia.edu/Documents/in/eCommerce_research">eCommerce research</a><script data-card-contents-for-ri="174320" type="text/json">{"id":174320,"name":"eCommerce research","url":"https://www.academia.edu/Documents/in/eCommerce_research?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14209333]'), work: {"id":14209333,"title":"Sentiment Analysis of Features in Review Mining","created_at":"2015-07-20T07:00:42.760-07:00","url":"https://www.academia.edu/14209333/Sentiment_Analysis_of_Features_in_Review_Mining?f_ri=5379","dom_id":"work_14209333","summary":"Apparently, Amazon is the leader in online shopping. Huge customer database, Enormous options to buy and trust makes amazon a better place to shop. Customer reviews are the one of the most influencing factors which decides the future of the product, whether the customer is going to buy that product or not. The objective of writing this paper is to provide an extra lineament for the product review system of amazon. In this paper we come up with the idea of sentiment analysis of features in review mining and consolidate the précis of review. We suggest the idea of extracting the polarity of the given set of features from the reviews so that we can provide a better summarized reviews of the product to the customer in term of features. For this purpose we are using different sentimental analysis algorithms for comparing the outputs. Our system automatically separates the review data into objective and subjective information. And also apply POS Tagging to refine our subjective data. We have used this refined subjective data as an input in our experiment. For experimental purpose we have trained subjective review data with SVM Classifier in WEKA. We also used stop-word list to reduce the noise while classifying the review strings. In Evaluation we tested the test data with different tunned parameters like folds and percentage split and also did the cross validation for test data set. Evaluation results shows 95% of data were classified correctly in all test data set. Based on our trained data set new review feature values will be predicted. Finally our system calculates features score for each product. The aim of this paper is to inform future buyers of the product what aspects of the product are good and bad according to the previous buyers. This is indeed very valuable information.","downloadable_attachments":[{"id":38237742,"asset_id":14209333,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33195352,"first_name":"Shivam","last_name":"Agrawal","domain_name":"independent","page_name":"ShivamAgrawal10","display_name":"Shivam Agrawal","profile_url":"https://independent.academia.edu/ShivamAgrawal10?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13690,"name":"Ecommerce","url":"https://www.academia.edu/Documents/in/Ecommerce?f_ri=5379","nofollow":true},{"id":174320,"name":"eCommerce research","url":"https://www.academia.edu/Documents/in/eCommerce_research?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8109511" data-work_id="8109511" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8109511/El_sentimiento_en_Zubiri">El sentimiento en Zubiri</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">La reflexión zubiriana sobre el sentimiento, se encamina a dilucidar la esencia del sentimiento estético mediante tres pasos sucesivos: en primer lugar, establecer qué es un sentimiento; en segundo lugar, indicar cuál es la relación... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8109511" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">La reflexión zubiriana sobre el sentimiento, se encamina a dilucidar la esencia del sentimiento estético mediante tres pasos sucesivos: en primer lugar, establecer qué es un sentimiento; en segundo lugar, indicar cuál es la relación entre el sentimiento y la realidad; en tercer y último lugar, abordar en qué consiste el sentimiento estético. En nuestro estudio nos limitaremos a las dos primeras etapas, pues nos parece que, a pesar de sus penetrantes intuiciones, Zubiri no ha desvelado la esencia del sentimiento por falta de una tercera pregunta que debería añadirse a las dos primeras: ¿por qué el sentimiento supone una relación con la realidad? La respuesta a esta cuestión constituirá el objetivo de este breve ensayo.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8109511" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="047f18483f8436169d8df1719ecc20b9" rel="nofollow" data-download="{"attachment_id":34556735,"asset_id":8109511,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34556735/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2839975" href="https://pusc.academia.edu/AntonioMalo">Antonio Malo</a><script data-card-contents-for-user="2839975" type="text/json">{"id":2839975,"first_name":"Antonio","last_name":"Malo","domain_name":"pusc","page_name":"AntonioMalo","display_name":"Antonio Malo","profile_url":"https://pusc.academia.edu/AntonioMalo?f_ri=5379","photo":"https://0.academia-photos.com/2839975/931439/1166341/s65_antonio.malo.jpg"}</script></span></span></li><li class="js-paper-rank-work_8109511 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8109511"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8109511, container: ".js-paper-rank-work_8109511", }); });</script></li><li class="js-percentile-work_8109511 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8109511; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_8109511"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_8109511 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="8109511"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8109511; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8109511]").text(description); $(".js-view-count-work_8109511").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8109511").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8109511"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="254" rel="nofollow" href="https://www.academia.edu/Documents/in/Emotion">Emotion</a>, <script data-card-contents-for-ri="254" type="text/json">{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="21498" rel="nofollow" href="https://www.academia.edu/Documents/in/Affect_Emotion">Affect/Emotion</a><script data-card-contents-for-ri="21498" type="text/json">{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8109511]'), work: {"id":8109511,"title":"El sentimiento en Zubiri","created_at":"2014-08-27T20:32:13.675-07:00","url":"https://www.academia.edu/8109511/El_sentimiento_en_Zubiri?f_ri=5379","dom_id":"work_8109511","summary":"La reflexión zubiriana sobre el sentimiento, se encamina a dilucidar la esencia del sentimiento estético mediante tres pasos sucesivos: en primer lugar, establecer qué es un sentimiento; en segundo lugar, indicar cuál es la relación entre el sentimiento y la realidad; en tercer y último lugar, abordar en qué consiste el sentimiento estético. En nuestro estudio nos limitaremos a las dos primeras etapas, pues nos parece que, a pesar de sus penetrantes intuiciones, Zubiri no ha desvelado la esencia del sentimiento por falta de una tercera pregunta que debería añadirse a las dos primeras: ¿por qué el sentimiento supone una relación con la realidad? La respuesta a esta cuestión constituirá el objetivo de este breve ensayo.","downloadable_attachments":[{"id":34556735,"asset_id":8109511,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2839975,"first_name":"Antonio","last_name":"Malo","domain_name":"pusc","page_name":"AntonioMalo","display_name":"Antonio Malo","profile_url":"https://pusc.academia.edu/AntonioMalo?f_ri=5379","photo":"https://0.academia-photos.com/2839975/931439/1166341/s65_antonio.malo.jpg"}],"research_interests":[{"id":254,"name":"Emotion","url":"https://www.academia.edu/Documents/in/Emotion?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":21498,"name":"Affect/Emotion","url":"https://www.academia.edu/Documents/in/Affect_Emotion?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48545803" data-work_id="48545803" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48545803/Sentiment_strength_detection_in_short_informal_text">Sentiment strength detection in short informal text</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48545803" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48545803" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="510dec776c344b996942bcbcdeaefa6c" rel="nofollow" data-download="{"attachment_id":67101673,"asset_id":48545803,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67101673/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1072775" href="https://constructor.academia.edu/ArvidKappas">Arvid Kappas</a><script data-card-contents-for-user="1072775" type="text/json">{"id":1072775,"first_name":"Arvid","last_name":"Kappas","domain_name":"constructor","page_name":"ArvidKappas","display_name":"Arvid Kappas","profile_url":"https://constructor.academia.edu/ArvidKappas?f_ri=5379","photo":"https://0.academia-photos.com/1072775/371220/449813/s65_arvid.kappas.jpg"}</script></span></span></li><li class="js-paper-rank-work_48545803 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48545803"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48545803, container: ".js-paper-rank-work_48545803", }); });</script></li><li class="js-percentile-work_48545803 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48545803; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48545803"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48545803 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48545803"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48545803; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48545803]").text(description); $(".js-view-count-work_48545803").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48545803").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48545803"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="37" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48545803]'), work: {"id":48545803,"title":"Sentiment strength detection in short informal text","created_at":"2021-05-04T22:51:43.105-07:00","url":"https://www.academia.edu/48545803/Sentiment_strength_detection_in_short_informal_text?f_ri=5379","dom_id":"work_48545803","summary":"A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.","downloadable_attachments":[{"id":67101673,"asset_id":48545803,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1072775,"first_name":"Arvid","last_name":"Kappas","domain_name":"constructor","page_name":"ArvidKappas","display_name":"Arvid Kappas","profile_url":"https://constructor.academia.edu/ArvidKappas?f_ri=5379","photo":"https://0.academia-photos.com/1072775/371220/449813/s65_arvid.kappas.jpg"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction?f_ri=5379"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=5379"},{"id":50238,"name":"Affect","url":"https://www.academia.edu/Documents/in/Affect?f_ri=5379"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies?f_ri=5379"},{"id":161176,"name":"The","url":"https://www.academia.edu/Documents/in/The?f_ri=5379"},{"id":1863718,"name":"The American","url":"https://www.academia.edu/Documents/in/The_American?f_ri=5379"},{"id":2213585,"name":"Information System","url":"https://www.academia.edu/Documents/in/Information_System?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36293472 coauthored" data-work_id="36293472" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36293472/_London2012_Towards_Citizen_Contributed_Urban_Planning_Through_Sentiment_Analysis_of_Twitter_Data">#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36293472" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36293472" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8e1ef74f1292ddcb20fdca96021210dd" rel="nofollow" data-download="{"attachment_id":56199855,"asset_id":36293472,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56199855/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39547741" href="https://sbg.academia.edu/annagyori">Anna Kovacs-Gyori</a><script data-card-contents-for-user="39547741" type="text/json">{"id":39547741,"first_name":"Anna","last_name":"Kovacs-Gyori","domain_name":"sbg","page_name":"annagyori","display_name":"Anna Kovacs-Gyori","profile_url":"https://sbg.academia.edu/annagyori?f_ri=5379","photo":"https://gravatar.com/avatar/11aac9aba2002553fd58587c46615ec4?s=65"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-36293472">+2</span><div class="hidden js-additional-users-36293472"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://sbg.academia.edu/AlinaRistea">Alina Ristea</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/PabloCabreraBarona">Pablo Cabrera-Barona</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-36293472'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-36293472').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_36293472 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36293472"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36293472, container: ".js-paper-rank-work_36293472", }); });</script></li><li class="js-percentile-work_36293472 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36293472; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36293472"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36293472 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36293472"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36293472; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36293472]").text(description); $(".js-view-count-work_36293472").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36293472").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36293472"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1362" rel="nofollow" href="https://www.academia.edu/Documents/in/Spatial_Analysis">Spatial Analysis</a>, <script data-card-contents-for-ri="1362" type="text/json">{"id":1362,"name":"Spatial Analysis","url":"https://www.academia.edu/Documents/in/Spatial_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4808" rel="nofollow" href="https://www.academia.edu/Documents/in/Urban_Planning">Urban Planning</a>, <script data-card-contents-for-ri="4808" type="text/json">{"id":4808,"name":"Urban Planning","url":"https://www.academia.edu/Documents/in/Urban_Planning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a><script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36293472]'), work: {"id":36293472,"title":"#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data","created_at":"2018-03-30T14:11:22.452-07:00","url":"https://www.academia.edu/36293472/_London2012_Towards_Citizen_Contributed_Urban_Planning_Through_Sentiment_Analysis_of_Twitter_Data?f_ri=5379","dom_id":"work_36293472","summary":"The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.","downloadable_attachments":[{"id":56199855,"asset_id":36293472,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39547741,"first_name":"Anna","last_name":"Kovacs-Gyori","domain_name":"sbg","page_name":"annagyori","display_name":"Anna Kovacs-Gyori","profile_url":"https://sbg.academia.edu/annagyori?f_ri=5379","photo":"https://gravatar.com/avatar/11aac9aba2002553fd58587c46615ec4?s=65"},{"id":11374394,"first_name":"Alina","last_name":"Ristea","domain_name":"sbg","page_name":"AlinaRistea","display_name":"Alina Ristea","profile_url":"https://sbg.academia.edu/AlinaRistea?f_ri=5379","photo":"/images/s65_no_pic.png"},{"id":200138,"first_name":"Pablo","last_name":"Cabrera-Barona","domain_name":"independent","page_name":"PabloCabreraBarona","display_name":"Pablo Cabrera-Barona","profile_url":"https://independent.academia.edu/PabloCabreraBarona?f_ri=5379","photo":"https://0.academia-photos.com/200138/10137040/11311285/s65_pablo.cabrera_barona.jpg"}],"research_interests":[{"id":1362,"name":"Spatial Analysis","url":"https://www.academia.edu/Documents/in/Spatial_Analysis?f_ri=5379","nofollow":true},{"id":4808,"name":"Urban Planning","url":"https://www.academia.edu/Documents/in/Urban_Planning?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":28120,"name":"Spatio Temporal Analysis","url":"https://www.academia.edu/Documents/in/Spatio_Temporal_Analysis?f_ri=5379"},{"id":119150,"name":"Topic modeling","url":"https://www.academia.edu/Documents/in/Topic_modeling?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14599842" data-work_id="14599842" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14599842/Sentiment_analysis_of_user_comments_for_one_class_collaborative_filtering_over_TED_talks">Sentiment analysis of user comments for one-class collaborative filtering over TED talks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">User-generated texts such as reviews, comments or discussions are valuable indicators of users' preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14599842" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">User-generated texts such as reviews, comments or discussions are valuable indicators of users' preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by explicit rating labels. We investigate their utility for a one-class collaborative filtering task such as bookmarking, where only the user actions are given as ground truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly, by more than 25% on unseen data, several competitive baselines.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14599842" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8f2e34cc714ff8bdbf4e57ee0660e0dd" rel="nofollow" data-download="{"attachment_id":44043996,"asset_id":14599842,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44043996/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33554466" href="https://heig-vd.academia.edu/AndreiPopescubelis">Andrei Popescu-belis</a><script data-card-contents-for-user="33554466" type="text/json">{"id":33554466,"first_name":"Andrei","last_name":"Popescu-belis","domain_name":"heig-vd","page_name":"AndreiPopescubelis","display_name":"Andrei Popescu-belis","profile_url":"https://heig-vd.academia.edu/AndreiPopescubelis?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_14599842 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14599842"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14599842, container: ".js-paper-rank-work_14599842", }); });</script></li><li class="js-percentile-work_14599842 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 14599842; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_14599842"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_14599842 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="14599842"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 14599842; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=14599842]").text(description); $(".js-view-count-work_14599842").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14599842").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14599842"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="77193" rel="nofollow" href="https://www.academia.edu/Documents/in/Collaborative_Filtering">Collaborative Filtering</a><script data-card-contents-for-ri="77193" type="text/json">{"id":77193,"name":"Collaborative Filtering","url":"https://www.academia.edu/Documents/in/Collaborative_Filtering?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14599842]'), work: {"id":14599842,"title":"Sentiment analysis of user comments for one-class collaborative filtering over TED talks","created_at":"2015-08-03T00:12:19.673-07:00","url":"https://www.academia.edu/14599842/Sentiment_analysis_of_user_comments_for_one_class_collaborative_filtering_over_TED_talks?f_ri=5379","dom_id":"work_14599842","summary":"User-generated texts such as reviews, comments or discussions are valuable indicators of users' preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by explicit rating labels. We investigate their utility for a one-class collaborative filtering task such as bookmarking, where only the user actions are given as ground truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly, by more than 25% on unseen data, several competitive baselines.","downloadable_attachments":[{"id":44043996,"asset_id":14599842,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33554466,"first_name":"Andrei","last_name":"Popescu-belis","domain_name":"heig-vd","page_name":"AndreiPopescubelis","display_name":"Andrei Popescu-belis","profile_url":"https://heig-vd.academia.edu/AndreiPopescubelis?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":77193,"name":"Collaborative Filtering","url":"https://www.academia.edu/Documents/in/Collaborative_Filtering?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42830607" data-work_id="42830607" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42830607/Twitter_Sentiment_Analysis_of_the_Indian_Union_Budget">Twitter Sentiment Analysis of the Indian Union Budget</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The presented study conducts a real-time sentiment analysis of the public reaction towards the announcement of the Indian Union Budget 2020. On social media platforms, the general public vents their opinions regarding popular issues. A... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42830607" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The presented study conducts a real-time sentiment analysis of the public reaction towards the announcement of the Indian Union Budget 2020. On social media platforms, the general public vents their opinions regarding popular issues. A total of 6000 tweets were mined to gauge the quick response of the public sentiment about the union budget which was presented in the Indian Parliament on February 1, 2020, at 11 am. The instantaneous reaction of the general public in the form of tweets was mined during the budget announcement hours. A sentiment analysis based model was used to analyze the dataset. Subjectivity and polarity values obtained for the dataset were used to calculate the sentiment for individual tweets. The experiment produced an overall positive score of +149.3387 based on the Twitter user’s collective opinions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42830607" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="762f54badd3453c1b10b68fda2313249" rel="nofollow" data-download="{"attachment_id":63063593,"asset_id":42830607,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63063593/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25055347" href="https://ccpmohali.academia.edu/SandeepRanjan">Sandeep Ranjan</a><script data-card-contents-for-user="25055347" type="text/json">{"id":25055347,"first_name":"Sandeep","last_name":"Ranjan","domain_name":"ccpmohali","page_name":"SandeepRanjan","display_name":"Sandeep Ranjan","profile_url":"https://ccpmohali.academia.edu/SandeepRanjan?f_ri=5379","photo":"https://0.academia-photos.com/25055347/6802691/7679951/s65_sandeep.ranjan.jpg"}</script></span></span></li><li class="js-paper-rank-work_42830607 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42830607"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42830607, container: ".js-paper-rank-work_42830607", }); });</script></li><li class="js-percentile-work_42830607 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42830607; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_42830607"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_42830607 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="42830607"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42830607; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42830607]").text(description); $(".js-view-count-work_42830607").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42830607").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42830607"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13739" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Network_Analysis_SNA_">Social Network Analysis (SNA)</a><script data-card-contents-for-ri="13739" type="text/json">{"id":13739,"name":"Social Network Analysis (SNA)","url":"https://www.academia.edu/Documents/in/Social_Network_Analysis_SNA_?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42830607]'), work: {"id":42830607,"title":"Twitter Sentiment Analysis of the Indian Union Budget","created_at":"2020-04-23T06:28:18.815-07:00","url":"https://www.academia.edu/42830607/Twitter_Sentiment_Analysis_of_the_Indian_Union_Budget?f_ri=5379","dom_id":"work_42830607","summary":"The presented study conducts a real-time sentiment analysis of the public reaction towards the announcement of the Indian Union Budget 2020. On social media platforms, the general public vents their opinions regarding popular issues. A total of 6000 tweets were mined to gauge the quick response of the public sentiment about the union budget which was presented in the Indian Parliament on February 1, 2020, at 11 am. The instantaneous reaction of the general public in the form of tweets was mined during the budget announcement hours. A sentiment analysis based model was used to analyze the dataset. Subjectivity and polarity values obtained for the dataset were used to calculate the sentiment for individual tweets. The experiment produced an overall positive score of +149.3387 based on the Twitter user’s collective opinions.","downloadable_attachments":[{"id":63063593,"asset_id":42830607,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":25055347,"first_name":"Sandeep","last_name":"Ranjan","domain_name":"ccpmohali","page_name":"SandeepRanjan","display_name":"Sandeep Ranjan","profile_url":"https://ccpmohali.academia.edu/SandeepRanjan?f_ri=5379","photo":"https://0.academia-photos.com/25055347/6802691/7679951/s65_sandeep.ranjan.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13739,"name":"Social Network Analysis (SNA)","url":"https://www.academia.edu/Documents/in/Social_Network_Analysis_SNA_?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_77589091" data-work_id="77589091" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/77589091/A_Large_Scale_Tweet_Dataset_for_Urdu_Text_Sentiment_Analysis">A Large Scale Tweet Dataset for Urdu Text Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_77589091" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/77589091" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f14665fc9c5da0e79eb40675faae30a4" rel="nofollow" data-download="{"attachment_id":84865647,"asset_id":77589091,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84865647/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="132428703" href="https://ntnu-no.academia.edu/AImran">Ali Shariq Imran</a><script data-card-contents-for-user="132428703" type="text/json">{"id":132428703,"first_name":"Ali Shariq","last_name":"Imran","domain_name":"ntnu-no","page_name":"AImran","display_name":"Ali Shariq Imran","profile_url":"https://ntnu-no.academia.edu/AImran?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_77589091 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="77589091"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 77589091, container: ".js-paper-rank-work_77589091", }); });</script></li><li class="js-percentile-work_77589091 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77589091; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_77589091"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_77589091 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="77589091"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77589091; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77589091]").text(description); $(".js-view-count-work_77589091").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_77589091").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="77589091"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77589091]'), work: {"id":77589091,"title":"A Large Scale Tweet Dataset for Urdu Text Sentiment Analysis","created_at":"2022-04-25T10:12:12.055-07:00","url":"https://www.academia.edu/77589091/A_Large_Scale_Tweet_Dataset_for_Urdu_Text_Sentiment_Analysis?f_ri=5379","dom_id":"work_77589091","summary":"This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.","downloadable_attachments":[{"id":84865647,"asset_id":77589091,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":132428703,"first_name":"Ali Shariq","last_name":"Imran","domain_name":"ntnu-no","page_name":"AImran","display_name":"Ali Shariq Imran","profile_url":"https://ntnu-no.academia.edu/AImran?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":77444,"name":"Urdu","url":"https://www.academia.edu/Documents/in/Urdu?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75395318" data-work_id="75395318" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75395318/Twitter_Sentiment_to_Analyze_Net_Brand_Reputation_of_Mobile_Phone_Providers">Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We may see competition among mobile providers to acquire new customers through campaign and advertisement war, especially on social media. The problem arises on how to measure the brand reputation of these providers based on people... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75395318" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We may see competition among mobile providers to acquire new customers through campaign and advertisement war, especially on social media. The problem arises on how to measure the brand reputation of these providers based on people response on their services quality. This paper addresses this issue by measuring brand reputation based on customer satisfaction through customer's sentiment analysis from Twitter data. Sample model is built and extracted from 10.000 raw Twitter messages data from January to March 2015 of top three mobile providers in Indonesia. We compared several features extractions, algorithms, and the classification schemes. After data cleaning and data balancing, the sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. We measure customer satisfaction on five products: 3G, 4G, Short Messaging, Voice and Internet services. This paper also discusses some correlated business insights in a telecommunication services industry. Based on the overall comparison of these five products, the NBR scores for PT XL Axiata Tbk, PT Telkomsel Tbk, and PT Indosat Tbk are 32.3%, 19.0%, and 10.9% respectively.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75395318" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d84363f4a5d2ecaa569c2628a76e68bd" rel="nofollow" data-download="{"attachment_id":83180807,"asset_id":75395318,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83180807/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30490361" href="https://independent.academia.edu/nurazizahvidya">nurazizah vidya</a><script data-card-contents-for-user="30490361" type="text/json">{"id":30490361,"first_name":"nurazizah","last_name":"vidya","domain_name":"independent","page_name":"nurazizahvidya","display_name":"nurazizah vidya","profile_url":"https://independent.academia.edu/nurazizahvidya?f_ri=5379","photo":"https://0.academia-photos.com/30490361/10580214/11808983/s65_nurazizah.vidya.jpg"}</script></span></span></li><li class="js-paper-rank-work_75395318 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75395318"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75395318, container: ".js-paper-rank-work_75395318", }); 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$(".js-view-count[data-work-id=75395318]").text(description); $(".js-view-count-work_75395318").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75395318").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75395318"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="923" rel="nofollow" href="https://www.academia.edu/Documents/in/Technology">Technology</a>, <script data-card-contents-for-ri="923" type="text/json">{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a><script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75395318]'), work: {"id":75395318,"title":"Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers","created_at":"2022-04-04T01:32:19.744-07:00","url":"https://www.academia.edu/75395318/Twitter_Sentiment_to_Analyze_Net_Brand_Reputation_of_Mobile_Phone_Providers?f_ri=5379","dom_id":"work_75395318","summary":"We may see competition among mobile providers to acquire new customers through campaign and advertisement war, especially on social media. The problem arises on how to measure the brand reputation of these providers based on people response on their services quality. This paper addresses this issue by measuring brand reputation based on customer satisfaction through customer's sentiment analysis from Twitter data. Sample model is built and extracted from 10.000 raw Twitter messages data from January to March 2015 of top three mobile providers in Indonesia. We compared several features extractions, algorithms, and the classification schemes. After data cleaning and data balancing, the sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. We measure customer satisfaction on five products: 3G, 4G, Short Messaging, Voice and Internet services. This paper also discusses some correlated business insights in a telecommunication services industry. Based on the overall comparison of these five products, the NBR scores for PT XL Axiata Tbk, PT Telkomsel Tbk, and PT Indosat Tbk are 32.3%, 19.0%, and 10.9% respectively.","downloadable_attachments":[{"id":83180807,"asset_id":75395318,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30490361,"first_name":"nurazizah","last_name":"vidya","domain_name":"independent","page_name":"nurazizahvidya","display_name":"nurazizah vidya","profile_url":"https://independent.academia.edu/nurazizahvidya?f_ri=5379","photo":"https://0.academia-photos.com/30490361/10580214/11808983/s65_nurazizah.vidya.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379"},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379"},{"id":6132,"name":"Soft Computing","url":"https://www.academia.edu/Documents/in/Soft_Computing?f_ri=5379"},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379"},{"id":13873,"name":"Twitter","url":"https://www.academia.edu/Documents/in/Twitter?f_ri=5379"},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=5379"},{"id":141502,"name":"Inteligencia artificial","url":"https://www.academia.edu/Documents/in/Inteligencia_artificial-1?f_ri=5379"},{"id":201685,"name":"Opinion Mining","url":"https://www.academia.edu/Documents/in/Opinion_Mining?f_ri=5379"},{"id":455746,"name":"OPINION MINING AND SENTIMENT ANALYSIS","url":"https://www.academia.edu/Documents/in/OPINION_MINING_AND_SENTIMENT_ANALYSIS?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47910789" data-work_id="47910789" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47910789/Exerting_2D_Space_of_Sentiment_Lexicons_with_Machine_Learning_Techniques_A_Hybrid_Approach_for_Sentiment_Analysis">Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_47910789" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms. Whereas the unsupervised sentiment lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other. In this paper, we propose a hybrid approach that uses unsupervised sentiment lexicons to transform the term space into a twodimensional sentiment space on which a discriminative classifier is trained in a supervised fashion. This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches. It is more suitable for scenarios where training data is scarce. We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47910789" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="88efc304acb6d02c8fbf02e4da1563a5" rel="nofollow" data-download="{"attachment_id":66795325,"asset_id":47910789,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66795325/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="54631842" href="https://maju.academia.edu/MuhammadYaseenKhan">Muhammad Yaseen Khan</a><script data-card-contents-for-user="54631842" type="text/json">{"id":54631842,"first_name":"Muhammad Yaseen","last_name":"Khan","domain_name":"maju","page_name":"MuhammadYaseenKhan","display_name":"Muhammad Yaseen Khan","profile_url":"https://maju.academia.edu/MuhammadYaseenKhan?f_ri=5379","photo":"https://0.academia-photos.com/54631842/38592502/32234225/s65_muhammad_yaseen.khan.jpeg"}</script></span></span></li><li class="js-paper-rank-work_47910789 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="47910789"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 47910789, container: ".js-paper-rank-work_47910789", }); });</script></li><li class="js-percentile-work_47910789 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 47910789; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_47910789"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_47910789 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="47910789"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 47910789; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=47910789]").text(description); $(".js-view-count-work_47910789").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_47910789").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="47910789"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="27006" rel="nofollow" href="https://www.academia.edu/Documents/in/Supervised_Learning_Techniques">Supervised Learning Techniques</a><script data-card-contents-for-ri="27006" type="text/json">{"id":27006,"name":"Supervised Learning Techniques","url":"https://www.academia.edu/Documents/in/Supervised_Learning_Techniques?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=47910789]'), work: {"id":47910789,"title":"Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis","created_at":"2021-05-03T05:47:49.222-07:00","url":"https://www.academia.edu/47910789/Exerting_2D_Space_of_Sentiment_Lexicons_with_Machine_Learning_Techniques_A_Hybrid_Approach_for_Sentiment_Analysis?f_ri=5379","dom_id":"work_47910789","summary":"Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms. Whereas the unsupervised sentiment lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other. In this paper, we propose a hybrid approach that uses unsupervised sentiment lexicons to transform the term space into a twodimensional sentiment space on which a discriminative classifier is trained in a supervised fashion. This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches. It is more suitable for scenarios where training data is scarce. We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.","downloadable_attachments":[{"id":66795325,"asset_id":47910789,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":54631842,"first_name":"Muhammad Yaseen","last_name":"Khan","domain_name":"maju","page_name":"MuhammadYaseenKhan","display_name":"Muhammad Yaseen Khan","profile_url":"https://maju.academia.edu/MuhammadYaseenKhan?f_ri=5379","photo":"https://0.academia-photos.com/54631842/38592502/32234225/s65_muhammad_yaseen.khan.jpeg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":27006,"name":"Supervised Learning Techniques","url":"https://www.academia.edu/Documents/in/Supervised_Learning_Techniques?f_ri=5379","nofollow":true},{"id":50390,"name":"Lexicon","url":"https://www.academia.edu/Documents/in/Lexicon?f_ri=5379"},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification?f_ri=5379"},{"id":377471,"name":"Hybrid Approach","url":"https://www.academia.edu/Documents/in/Hybrid_Approach?f_ri=5379"},{"id":1662853,"name":"Lexicon Based Sentimental Analysis","url":"https://www.academia.edu/Documents/in/Lexicon_Based_Sentimental_Analysis?f_ri=5379"},{"id":1955068,"name":"Sentiment Lexicon","url":"https://www.academia.edu/Documents/in/Sentiment_Lexicon?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43685479" data-work_id="43685479" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43685479/Convolutional_Multi_Head_Self_Attention_on_Memory_for_Aspect_Sentiment_Classification">Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network (CMA-MemNet). This is an improved model based on memory networks, and makes it possible to extract... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43685479" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network (CMA-MemNet). This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects. In order to fix the memory network’s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information. We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself. Meanwhile, unlike most recurrent neural network (RNN) long short term memory (LSTM), gated recurrent unit (GRU) models, we retain the parallelism of the network. We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6. Compared with some popular baseline methods, our model performs excellently.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43685479" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7622dc302b1845cc4c08f7f1042383cc" rel="nofollow" data-download="{"attachment_id":63988105,"asset_id":43685479,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63988105/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="53180586" href="https://independent.academia.edu/IEEECAAJAS">IEEE/CAA J. Autom. Sinica</a><script data-card-contents-for-user="53180586" type="text/json">{"id":53180586,"first_name":"IEEE/CAA","last_name":"J. Autom. Sinica","domain_name":"independent","page_name":"IEEECAAJAS","display_name":"IEEE/CAA J. Autom. Sinica","profile_url":"https://independent.academia.edu/IEEECAAJAS?f_ri=5379","photo":"https://0.academia-photos.com/53180586/14177798/22465909/s65_ieee_caa.j._autom._sinica.jpg"}</script></span></span></li><li class="js-paper-rank-work_43685479 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43685479"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43685479, container: ".js-paper-rank-work_43685479", }); });</script></li><li class="js-percentile-work_43685479 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43685479; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43685479"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43685479 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43685479"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43685479; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43685479]").text(description); $(".js-view-count-work_43685479").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43685479").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43685479"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="81182" rel="nofollow" href="https://www.academia.edu/Documents/in/Deep_Learning">Deep Learning</a>, <script data-card-contents-for-ri="81182" type="text/json">{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2667295" rel="nofollow" href="https://www.academia.edu/Documents/in/Memory_Networks">Memory Networks</a><script data-card-contents-for-ri="2667295" type="text/json">{"id":2667295,"name":"Memory Networks","url":"https://www.academia.edu/Documents/in/Memory_Networks?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43685479]'), work: {"id":43685479,"title":"Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification","created_at":"2020-07-21T22:37:30.500-07:00","url":"https://www.academia.edu/43685479/Convolutional_Multi_Head_Self_Attention_on_Memory_for_Aspect_Sentiment_Classification?f_ri=5379","dom_id":"work_43685479","summary":"This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network (CMA-MemNet). This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects. In order to fix the memory network’s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information. We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself. Meanwhile, unlike most recurrent neural network (RNN) long short term memory (LSTM), gated recurrent unit (GRU) models, we retain the parallelism of the network. We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6. Compared with some popular baseline methods, our model performs excellently.","downloadable_attachments":[{"id":63988105,"asset_id":43685479,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":53180586,"first_name":"IEEE/CAA","last_name":"J. Autom. Sinica","domain_name":"independent","page_name":"IEEECAAJAS","display_name":"IEEE/CAA J. Autom. Sinica","profile_url":"https://independent.academia.edu/IEEECAAJAS?f_ri=5379","photo":"https://0.academia-photos.com/53180586/14177798/22465909/s65_ieee_caa.j._autom._sinica.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning?f_ri=5379","nofollow":true},{"id":2667295,"name":"Memory Networks","url":"https://www.academia.edu/Documents/in/Memory_Networks?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32934838" data-work_id="32934838" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32934838/Do_the_political_opinions_shared_on_Social_Media_platform_Twitter_accurately_represent_the_political_opinions_of_the_general_populace">Do the political opinions shared on Social Media (platform Twitter) accurately represent the political opinions of the general populace?</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This research investigates the relationship between political opinions expressed on Social Media and the political opinions of the public (electorate) on major political event. Specifically, on the 2016 UK EU Referendum vote. It looks to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32934838" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This research investigates the relationship between political opinions expressed on Social Media and the political opinions of the public (electorate) on major political event. Specifically, on the 2016 UK EU Referendum vote. It looks to answer the question, are Social Media sites such as Twitter echo chambers of people with the same opinion or do they accurately represent the opinions of the public. The investigation includes a computer program that extracts a Big Data set of tweets from Twitter, then analyses them to find their binary sentiment using a democracy of multiple machine learning sentiment classifiers. This is then compared to a database of pollster data from a variety of pollsters to find any differences. It also investigates why there is or isn’t a difference in opinions between Twitter users and the public.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32934838" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="953c4d669635c77300b38e8b93e51044" rel="nofollow" data-download="{"attachment_id":53068006,"asset_id":32934838,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53068006/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="60108821" href="https://coventry.academia.edu/PhilipBell">Philip Bell</a><script data-card-contents-for-user="60108821" type="text/json">{"id":60108821,"first_name":"Philip","last_name":"Bell","domain_name":"coventry","page_name":"PhilipBell","display_name":"Philip Bell","profile_url":"https://coventry.academia.edu/PhilipBell?f_ri=5379","photo":"https://0.academia-photos.com/60108821/16650845/16948853/s65_philip.bell.jpg"}</script></span></span></li><li class="js-paper-rank-work_32934838 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32934838"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32934838, container: ".js-paper-rank-work_32934838", }); });</script></li><li class="js-percentile-work_32934838 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32934838; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_32934838"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_32934838 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="32934838"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32934838; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32934838]").text(description); $(".js-view-count-work_32934838").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_32934838").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="32934838"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=32934838]'), work: {"id":32934838,"title":"Do the political opinions shared on Social Media (platform Twitter) accurately represent the political opinions of the general populace?","created_at":"2017-05-10T04:06:40.394-07:00","url":"https://www.academia.edu/32934838/Do_the_political_opinions_shared_on_Social_Media_platform_Twitter_accurately_represent_the_political_opinions_of_the_general_populace?f_ri=5379","dom_id":"work_32934838","summary":"This research investigates the relationship between political opinions expressed on Social Media and the political opinions of the public (electorate) on major political event. Specifically, on the 2016 UK EU Referendum vote. It looks to answer the question, are Social Media sites such as Twitter echo chambers of people with the same opinion or do they accurately represent the opinions of the public. The investigation includes a computer program that extracts a Big Data set of tweets from Twitter, then analyses them to find their binary sentiment using a democracy of multiple machine learning sentiment classifiers. This is then compared to a database of pollster data from a variety of pollsters to find any differences. It also investigates why there is or isn’t a difference in opinions between Twitter users and the public. ","downloadable_attachments":[{"id":53068006,"asset_id":32934838,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":60108821,"first_name":"Philip","last_name":"Bell","domain_name":"coventry","page_name":"PhilipBell","display_name":"Philip Bell","profile_url":"https://coventry.academia.edu/PhilipBell?f_ri=5379","photo":"https://0.academia-photos.com/60108821/16650845/16948853/s65_philip.bell.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66476824" data-work_id="66476824" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66476824/Machine_Learning_Techniques_for_Sentiment_Analysis_A_Review">Machine Learning Techniques for Sentiment Analysis: A Review</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66476824" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66476824" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="de17d2c9d0cd26986afe6f8f126b9fe4" rel="nofollow" data-download="{"attachment_id":77653168,"asset_id":66476824,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77653168/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="136690096" href="https://independent.academia.edu/SunilMalviya3">Sunil Malviya</a><script data-card-contents-for-user="136690096" type="text/json">{"id":136690096,"first_name":"Sunil","last_name":"Malviya","domain_name":"independent","page_name":"SunilMalviya3","display_name":"Sunil Malviya","profile_url":"https://independent.academia.edu/SunilMalviya3?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_66476824 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66476824"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66476824, container: ".js-paper-rank-work_66476824", }); });</script></li><li class="js-percentile-work_66476824 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 66476824; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_66476824"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_66476824 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="66476824"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 66476824; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=66476824]").text(description); $(".js-view-count-work_66476824").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66476824").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66476824"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="296947" rel="nofollow" href="https://www.academia.edu/Documents/in/Special_Issue">Special Issue</a><script data-card-contents-for-ri="296947" type="text/json">{"id":296947,"name":"Special Issue","url":"https://www.academia.edu/Documents/in/Special_Issue?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66476824]'), work: {"id":66476824,"title":"Machine Learning Techniques for Sentiment Analysis: A Review","created_at":"2021-12-29T20:49:42.891-08:00","url":"https://www.academia.edu/66476824/Machine_Learning_Techniques_for_Sentiment_Analysis_A_Review?f_ri=5379","dom_id":"work_66476824","summary":"Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.","downloadable_attachments":[{"id":77653168,"asset_id":66476824,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":136690096,"first_name":"Sunil","last_name":"Malviya","domain_name":"independent","page_name":"SunilMalviya3","display_name":"Sunil Malviya","profile_url":"https://independent.academia.edu/SunilMalviya3?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":296947,"name":"Special Issue","url":"https://www.academia.edu/Documents/in/Special_Issue?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_40922301" data-work_id="40922301" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/40922301/Sentiment_Analysis_Using_Text_Mining_of_Indonesia_Tourism_Reviews_via_Social_Media">Sentiment Analysis Using Text Mining of Indonesia Tourism Reviews via Social Media</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Indonesian tourism industry continues to develop and has become the core of the nation economy. Indonesia is known for its wealth of natural beauty, which can be used as a potential for tourism business. Garut is a city in Indonesia... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_40922301" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Indonesian tourism industry continues to develop and has become the core of the nation economy. Indonesia is known for its wealth of natural beauty, which can be used as a potential for tourism business. Garut is a city in Indonesia known for its beach, mountains, and culinary arts. The purpose of this study is to create a priority map of tourist attractions that can be utilized by local governments. Sentiment analysis was used 413,175 netizen comments via the social media platforms Instagram and Google reviews. Data was collected from January 2018-February 2019. The results show that the number of positive comments is significantly higher than the number of negative comments. Beach tourism a serious priority; not only is it the most preferred tourist attraction, but also the type that gets the most negative comments. While the main problem for Garut Regency tourism is hygiene, garbage is either overlapping or scattered, preventing Garut from having all-around tourism charm instead of being superior only in the sector of natural beauty. The suggestions in this research can be used as proposals for improving and developing tourism to realize a dignified 'Garut Charm'.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/40922301" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2ec31ae090b0ae90993cf94ac44ddc4e" rel="nofollow" data-download="{"attachment_id":61210754,"asset_id":40922301,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61210754/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3504137" href="https://garutkab.academia.edu/DiniAlamanda">Dini T U R I P A N A M Alamanda</a><script data-card-contents-for-user="3504137" type="text/json">{"id":3504137,"first_name":"Dini","last_name":"Alamanda","domain_name":"garutkab","page_name":"DiniAlamanda","display_name":"Dini T U R I P A N A M Alamanda","profile_url":"https://garutkab.academia.edu/DiniAlamanda?f_ri=5379","photo":"https://0.academia-photos.com/3504137/8713677/15600876/s65_dini.alamanda.jpg"}</script></span></span></li><li class="js-paper-rank-work_40922301 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="40922301"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 40922301, container: ".js-paper-rank-work_40922301", }); });</script></li><li class="js-percentile-work_40922301 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40922301; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_40922301"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_40922301 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="40922301"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40922301; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40922301]").text(description); $(".js-view-count-work_40922301").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_40922301").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="40922301"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5639" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Mining">Text Mining</a>, <script data-card-contents-for-ri="5639" type="text/json">{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="426727" rel="nofollow" href="https://www.academia.edu/Documents/in/Pariwisata">Pariwisata</a><script data-card-contents-for-ri="426727" type="text/json">{"id":426727,"name":"Pariwisata","url":"https://www.academia.edu/Documents/in/Pariwisata?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=40922301]'), work: {"id":40922301,"title":"Sentiment Analysis Using Text Mining of Indonesia Tourism Reviews via Social Media","created_at":"2019-11-13T22:22:31.079-08:00","url":"https://www.academia.edu/40922301/Sentiment_Analysis_Using_Text_Mining_of_Indonesia_Tourism_Reviews_via_Social_Media?f_ri=5379","dom_id":"work_40922301","summary":"The Indonesian tourism industry continues to develop and has become the core of the nation economy. Indonesia is known for its wealth of natural beauty, which can be used as a potential for tourism business. Garut is a city in Indonesia known for its beach, mountains, and culinary arts. The purpose of this study is to create a priority map of tourist attractions that can be utilized by local governments. Sentiment analysis was used 413,175 netizen comments via the social media platforms Instagram and Google reviews. Data was collected from January 2018-February 2019. The results show that the number of positive comments is significantly higher than the number of negative comments. Beach tourism a serious priority; not only is it the most preferred tourist attraction, but also the type that gets the most negative comments. While the main problem for Garut Regency tourism is hygiene, garbage is either overlapping or scattered, preventing Garut from having all-around tourism charm instead of being superior only in the sector of natural beauty. The suggestions in this research can be used as proposals for improving and developing tourism to realize a dignified 'Garut Charm'.","downloadable_attachments":[{"id":61210754,"asset_id":40922301,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3504137,"first_name":"Dini","last_name":"Alamanda","domain_name":"garutkab","page_name":"DiniAlamanda","display_name":"Dini T U R I P A N A M Alamanda","profile_url":"https://garutkab.academia.edu/DiniAlamanda?f_ri=5379","photo":"https://0.academia-photos.com/3504137/8713677/15600876/s65_dini.alamanda.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":5639,"name":"Text Mining","url":"https://www.academia.edu/Documents/in/Text_Mining?f_ri=5379","nofollow":true},{"id":426727,"name":"Pariwisata","url":"https://www.academia.edu/Documents/in/Pariwisata?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19689499" data-work_id="19689499" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19689499/Valutare_e_recensire_in_lingua_italiana_analisi_linguistica_e_testuale_della_manifestazione_del_sentiment">Valutare e recensire in lingua italiana: analisi linguistica e testuale della manifestazione del sentiment</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19689499" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d1baf163cec1a1e7edeee88845773167" rel="nofollow" data-download="{"attachment_id":45473111,"asset_id":19689499,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45473111/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="899627" href="https://unimol.academia.edu/GiulianaFiorentino">Giuliana Fiorentino</a><script data-card-contents-for-user="899627" type="text/json">{"id":899627,"first_name":"Giuliana","last_name":"Fiorentino","domain_name":"unimol","page_name":"GiulianaFiorentino","display_name":"Giuliana Fiorentino","profile_url":"https://unimol.academia.edu/GiulianaFiorentino?f_ri=5379","photo":"https://0.academia-photos.com/899627/1006279/38447972/s65_giuliana.fiorentino.jpg"}</script></span></span></li><li class="js-paper-rank-work_19689499 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19689499"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19689499, container: ".js-paper-rank-work_19689499", }); });</script></li><li class="js-percentile-work_19689499 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19689499; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19689499"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19689499 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19689499"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19689499; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19689499]").text(description); $(".js-view-count-work_19689499").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19689499").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19689499"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="30946" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer-Mediated_Communication_CMC_">Computer-Mediated Communication (CMC)</a>, <script data-card-contents-for-ri="30946" type="text/json">{"id":30946,"name":"Computer-Mediated Communication (CMC)","url":"https://www.academia.edu/Documents/in/Computer-Mediated_Communication_CMC_?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="69691" rel="nofollow" href="https://www.academia.edu/Documents/in/CMC_Discourse_analysis_Online_interaction_analysis">CMC, Discourse analysis, Online interaction analysis</a><script data-card-contents-for-ri="69691" type="text/json">{"id":69691,"name":"CMC, Discourse analysis, Online interaction analysis","url":"https://www.academia.edu/Documents/in/CMC_Discourse_analysis_Online_interaction_analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19689499]'), work: {"id":19689499,"title":"Valutare e recensire in lingua italiana: analisi linguistica e testuale della manifestazione del sentiment","created_at":"2015-12-16T08:59:50.436-08:00","url":"https://www.academia.edu/19689499/Valutare_e_recensire_in_lingua_italiana_analisi_linguistica_e_testuale_della_manifestazione_del_sentiment?f_ri=5379","dom_id":"work_19689499","summary":null,"downloadable_attachments":[{"id":45473111,"asset_id":19689499,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":899627,"first_name":"Giuliana","last_name":"Fiorentino","domain_name":"unimol","page_name":"GiulianaFiorentino","display_name":"Giuliana Fiorentino","profile_url":"https://unimol.academia.edu/GiulianaFiorentino?f_ri=5379","photo":"https://0.academia-photos.com/899627/1006279/38447972/s65_giuliana.fiorentino.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":30946,"name":"Computer-Mediated Communication (CMC)","url":"https://www.academia.edu/Documents/in/Computer-Mediated_Communication_CMC_?f_ri=5379","nofollow":true},{"id":69691,"name":"CMC, Discourse analysis, Online interaction analysis","url":"https://www.academia.edu/Documents/in/CMC_Discourse_analysis_Online_interaction_analysis?f_ri=5379","nofollow":true},{"id":165615,"name":"Textual analysis","url":"https://www.academia.edu/Documents/in/Textual_analysis?f_ri=5379"},{"id":332698,"name":"Online Consumer Reviews","url":"https://www.academia.edu/Documents/in/Online_Consumer_Reviews?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50213566" data-work_id="50213566" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50213566/Facebook_an_Anti_Stereotyping_Tool_A_Case_Study">Facebook an Anti-Stereotyping Tool: A Case Study</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Facebook, the most popular social media (SM) platform has penetrated every nook and corner of the world. SM is now treated as the 'fifth Estate', other than legislative, executive, judiciary, and mainstream media. The power of SM as a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50213566" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Facebook, the most popular social media (SM) platform has penetrated every nook and corner of the world. SM is now treated as the 'fifth Estate', other than legislative, executive, judiciary, and mainstream media. The power of SM as a critique is widely acknowledged. Establishments are finding it difficult to deal with it at times. Due to its ease of usage and relative anonymity, the general public finds it very convenient to put across their viewpoints, even if it's against the establishment. Some establishments at times are at loggerheads with champions of freedom of speech including civil rights activists. SM has been used for propaganda, marketing, and awareness campaigns. In this paper, we are proposing to use this powerful tool towards social change. Through a case study, a detailed process is being proposed for using social media particularly Facebook as an an-ti-stereotyping tool. The response to an online survey, the outcome of opinion mining, and the enthusiastic response to our case study by the targeted audience validate our hypothesis that Facebook can be effectively utilized as an anti-stereotyping tool.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50213566" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c3c23e80969da25323acaf252499cc96" rel="nofollow" data-download="{"attachment_id":68283518,"asset_id":50213566,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68283518/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="174643068" href="https://independent.academia.edu/JIEEEA2ZJournals">Journal of Informatics Electrical and Electronics Engineering (JIEEE), A 2 Z Journals</a><script data-card-contents-for-user="174643068" type="text/json">{"id":174643068,"first_name":"Journal of Informatics Electrical and Electronics Engineering (JIEEE),","last_name":"Journals","domain_name":"independent","page_name":"JIEEEA2ZJournals","display_name":"Journal of Informatics Electrical and Electronics Engineering (JIEEE), A 2 Z Journals","profile_url":"https://independent.academia.edu/JIEEEA2ZJournals?f_ri=5379","photo":"https://0.academia-photos.com/174643068/54942113/53450373/s65_journal_of_informatics_electrical_and_electronics_engineering_jieee_.journals.png"}</script></span></span></li><li class="js-paper-rank-work_50213566 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50213566"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50213566, container: ".js-paper-rank-work_50213566", }); });</script></li><li class="js-percentile-work_50213566 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50213566; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_50213566"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_50213566 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="50213566"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50213566; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50213566]").text(description); $(".js-view-count-work_50213566").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_50213566").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50213566"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="100383" rel="nofollow" href="https://www.academia.edu/Documents/in/Stereotyping">Stereotyping</a><script data-card-contents-for-ri="100383" type="text/json">{"id":100383,"name":"Stereotyping","url":"https://www.academia.edu/Documents/in/Stereotyping?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50213566]'), work: {"id":50213566,"title":"Facebook an Anti-Stereotyping Tool: A Case Study","created_at":"2021-07-24T02:39:40.970-07:00","url":"https://www.academia.edu/50213566/Facebook_an_Anti_Stereotyping_Tool_A_Case_Study?f_ri=5379","dom_id":"work_50213566","summary":"Facebook, the most popular social media (SM) platform has penetrated every nook and corner of the world. SM is now treated as the 'fifth Estate', other than legislative, executive, judiciary, and mainstream media. The power of SM as a critique is widely acknowledged. Establishments are finding it difficult to deal with it at times. Due to its ease of usage and relative anonymity, the general public finds it very convenient to put across their viewpoints, even if it's against the establishment. Some establishments at times are at loggerheads with champions of freedom of speech including civil rights activists. SM has been used for propaganda, marketing, and awareness campaigns. In this paper, we are proposing to use this powerful tool towards social change. Through a case study, a detailed process is being proposed for using social media particularly Facebook as an an-ti-stereotyping tool. The response to an online survey, the outcome of opinion mining, and the enthusiastic response to our case study by the targeted audience validate our hypothesis that Facebook can be effectively utilized as an anti-stereotyping tool.","downloadable_attachments":[{"id":68283518,"asset_id":50213566,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":174643068,"first_name":"Journal of Informatics Electrical and Electronics Engineering (JIEEE),","last_name":"Journals","domain_name":"independent","page_name":"JIEEEA2ZJournals","display_name":"Journal of Informatics Electrical and Electronics Engineering (JIEEE), A 2 Z Journals","profile_url":"https://independent.academia.edu/JIEEEA2ZJournals?f_ri=5379","photo":"https://0.academia-photos.com/174643068/54942113/53450373/s65_journal_of_informatics_electrical_and_electronics_engineering_jieee_.journals.png"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":100383,"name":"Stereotyping","url":"https://www.academia.edu/Documents/in/Stereotyping?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42199505" data-work_id="42199505" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42199505/Systematic_scoping_review_on_social_media_monitoring_methods_and_interventions_relating_to_vaccine_hesitancy">Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Social media, understood as a means of communication as well as a space to socialise and interact, has become a common source for people to look for health information. Rather than consulting a single, authoritative source, they usually... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42199505" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Social media, understood as a means of communication as well as a space to socialise and interact, has become a common source for people to look for health information. Rather than consulting a single, authoritative source, they usually want a variety of opinions. At the same time, people are often exposed to information about vaccination online without necessarily looking for it.<br /><br />The associated amplification of risk and risk perception through social media, has led some countries and health authorities to start monitoring social media platforms in order to counter the spread of misinformation and rebuild public trust in vaccination.<br /><br />The purpose of this systematic scoping review was to analyse the social media monitoring techniques and interventions with the following objectives:<br /><br />- identify preferences: which social media platforms do users choose as a source of information on vaccination and how does that influence their perceptions of vaccination?<br />- identify different methods and tools for social media monitoring in the context of vaccination including their strengths and weaknesses.<br />review how such monitoring and information gathered from social media monitoring can be used to inform communication strategies around immunisation.<br />- identify the uses, benefits and limitations of social media as an intervention tool in relation to vaccination (i.e. how effective social media is as an intervention tool for increasing vaccination).<br /><br />An extensive database search led to the inclusion of 115 scientific articles in the review, looking at the use of social media, social media monitoring and (public health) interventions.<br /><br />The review results show for example that between 4 and 62% of various study populations in different countries use social media as a source of information on vaccination, with results varying by type of social media platform. Overall, Facebook was the most common social media resource for information on vaccination.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42199505" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1644017a8ef5302510e344bc8faf2eb3" rel="nofollow" data-download="{"attachment_id":62345218,"asset_id":42199505,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/62345218/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6688793" href="https://oxford.academia.edu/SMartin">Sam Martin</a><script data-card-contents-for-user="6688793" type="text/json">{"id":6688793,"first_name":"Sam","last_name":"Martin","domain_name":"oxford","page_name":"SMartin","display_name":"Sam Martin","profile_url":"https://oxford.academia.edu/SMartin?f_ri=5379","photo":"https://0.academia-photos.com/6688793/2636580/72712684/s65_sam.martin.png"}</script></span></span></li><li class="js-paper-rank-work_42199505 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42199505"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42199505, container: ".js-paper-rank-work_42199505", }); });</script></li><li class="js-percentile-work_42199505 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42199505; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_42199505"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_42199505 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="42199505"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42199505; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42199505]").text(description); $(".js-view-count-work_42199505").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42199505").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42199505"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1197" rel="nofollow" href="https://www.academia.edu/Documents/in/Digital_Humanities">Digital Humanities</a>, <script data-card-contents-for-ri="1197" type="text/json">{"id":1197,"name":"Digital Humanities","url":"https://www.academia.edu/Documents/in/Digital_Humanities?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4365" rel="nofollow" href="https://www.academia.edu/Documents/in/Vaccines">Vaccines</a>, <script data-card-contents-for-ri="4365" type="text/json">{"id":4365,"name":"Vaccines","url":"https://www.academia.edu/Documents/in/Vaccines?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a><script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42199505]'), work: {"id":42199505,"title":"Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy","created_at":"2020-03-12T01:50:36.590-07:00","url":"https://www.academia.edu/42199505/Systematic_scoping_review_on_social_media_monitoring_methods_and_interventions_relating_to_vaccine_hesitancy?f_ri=5379","dom_id":"work_42199505","summary":"Social media, understood as a means of communication as well as a space to socialise and interact, has become a common source for people to look for health information. Rather than consulting a single, authoritative source, they usually want a variety of opinions. At the same time, people are often exposed to information about vaccination online without necessarily looking for it.\n\nThe associated amplification of risk and risk perception through social media, has led some countries and health authorities to start monitoring social media platforms in order to counter the spread of misinformation and rebuild public trust in vaccination.\n\nThe purpose of this systematic scoping review was to analyse the social media monitoring techniques and interventions with the following objectives:\n\n- identify preferences: which social media platforms do users choose as a source of information on vaccination and how does that influence their perceptions of vaccination?\n- identify different methods and tools for social media monitoring in the context of vaccination including their strengths and weaknesses.\nreview how such monitoring and information gathered from social media monitoring can be used to inform communication strategies around immunisation.\n- identify the uses, benefits and limitations of social media as an intervention tool in relation to vaccination (i.e. how effective social media is as an intervention tool for increasing vaccination).\n\nAn extensive database search led to the inclusion of 115 scientific articles in the review, looking at the use of social media, social media monitoring and (public health) interventions.\n\nThe review results show for example that between 4 and 62% of various study populations in different countries use social media as a source of information on vaccination, with results varying by type of social media platform. Overall, Facebook was the most common social media resource for information on vaccination.","downloadable_attachments":[{"id":62345218,"asset_id":42199505,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6688793,"first_name":"Sam","last_name":"Martin","domain_name":"oxford","page_name":"SMartin","display_name":"Sam Martin","profile_url":"https://oxford.academia.edu/SMartin?f_ri=5379","photo":"https://0.academia-photos.com/6688793/2636580/72712684/s65_sam.martin.png"}],"research_interests":[{"id":1197,"name":"Digital Humanities","url":"https://www.academia.edu/Documents/in/Digital_Humanities?f_ri=5379","nofollow":true},{"id":4365,"name":"Vaccines","url":"https://www.academia.edu/Documents/in/Vaccines?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":19458,"name":"Public Relations \u0026 Social Media","url":"https://www.academia.edu/Documents/in/Public_Relations_and_Social_Media?f_ri=5379"},{"id":24444,"name":"Mixed Methods Research","url":"https://www.academia.edu/Documents/in/Mixed_Methods_Research?f_ri=5379"},{"id":150847,"name":"Vaccine","url":"https://www.academia.edu/Documents/in/Vaccine?f_ri=5379"},{"id":256465,"name":"Social Networking \u0026 Social Media","url":"https://www.academia.edu/Documents/in/Social_Networking_and_Social_Media?f_ri=5379"},{"id":260024,"name":"Digital Sociology","url":"https://www.academia.edu/Documents/in/Digital_Sociology?f_ri=5379"},{"id":515835,"name":"Social Media and Collaborative Technologies","url":"https://www.academia.edu/Documents/in/Social_Media_and_Collaborative_Technologies?f_ri=5379"},{"id":1409079,"name":"Vaccine Hesitancy","url":"https://www.academia.edu/Documents/in/Vaccine_Hesitancy?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36901160" data-work_id="36901160" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36901160/Movies_success_predictive_model_and_sentiment_analysis">Movies' success - predictive model and sentiment analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The film industry is one of the biggest contributors to the entertainment industry and also it is characterized with its unpredictability in success and Failure. Film Industry has always amused everyone with its unpredictable success and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36901160" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The film industry is one of the biggest contributors to the entertainment industry and also it is characterized with its unpredictability in success and Failure. Film Industry has always amused everyone with its unpredictable success and Failure.  This research looks into the inner details of watching a movie by splitting the research into three main components. First section is exploring the variables that influence the frequency of movie watch; second, developing a model to predict the success or failure. Finally, social network sentiment analysis is carried out through data mining to capture the audience sentiment and its impact on movie’s success and failure. The research tries to look at the success or failure of a movie on a more holistic manner than trying to grade the performance of a movie over a few variables based on the previous research works on movie success prediction.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36901160" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="722dc822d4fed1b4200a79139026acd6" rel="nofollow" data-download="{"attachment_id":57828454,"asset_id":36901160,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57828454/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="83389527" href="https://allianceu.academia.edu/prasannaMohanRaj">prasanna Mohan Raj</a><script data-card-contents-for-user="83389527" type="text/json">{"id":83389527,"first_name":"prasanna Mohan","last_name":"Raj","domain_name":"allianceu","page_name":"prasannaMohanRaj","display_name":"prasanna Mohan Raj","profile_url":"https://allianceu.academia.edu/prasannaMohanRaj?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_36901160 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36901160"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36901160, container: ".js-paper-rank-work_36901160", }); });</script></li><li class="js-percentile-work_36901160 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36901160; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36901160"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36901160 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36901160"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36901160; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36901160]").text(description); $(".js-view-count-work_36901160").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36901160").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36901160"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13258" rel="nofollow" href="https://www.academia.edu/Documents/in/Marketing_Research">Marketing Research</a>, <script data-card-contents-for-ri="13258" type="text/json">{"id":13258,"name":"Marketing Research","url":"https://www.academia.edu/Documents/in/Marketing_Research?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51168" rel="nofollow" href="https://www.academia.edu/Documents/in/Predictive_Analytics">Predictive Analytics</a><script data-card-contents-for-ri="51168" type="text/json">{"id":51168,"name":"Predictive Analytics","url":"https://www.academia.edu/Documents/in/Predictive_Analytics?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36901160]'), work: {"id":36901160,"title":"Movies' success - predictive model and sentiment analysis","created_at":"2018-06-23T04:22:06.013-07:00","url":"https://www.academia.edu/36901160/Movies_success_predictive_model_and_sentiment_analysis?f_ri=5379","dom_id":"work_36901160","summary":"The film industry is one of the biggest contributors to the entertainment industry and also it is characterized with its unpredictability in success and Failure. Film Industry has always amused everyone with its unpredictable success and Failure. This research looks into the inner details of watching a movie by splitting the research into three main components. First section is exploring the variables that influence the frequency of movie watch; second, developing a model to predict the success or failure. Finally, social network sentiment analysis is carried out through data mining to capture the audience sentiment and its impact on movie’s success and failure. The research tries to look at the success or failure of a movie on a more holistic manner than trying to grade the performance of a movie over a few variables based on the previous research works on movie success prediction.","downloadable_attachments":[{"id":57828454,"asset_id":36901160,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":83389527,"first_name":"prasanna Mohan","last_name":"Raj","domain_name":"allianceu","page_name":"prasannaMohanRaj","display_name":"prasanna Mohan Raj","profile_url":"https://allianceu.academia.edu/prasannaMohanRaj?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":13258,"name":"Marketing Research","url":"https://www.academia.edu/Documents/in/Marketing_Research?f_ri=5379","nofollow":true},{"id":51168,"name":"Predictive Analytics","url":"https://www.academia.edu/Documents/in/Predictive_Analytics?f_ri=5379","nofollow":true},{"id":51359,"name":"Business Analytics","url":"https://www.academia.edu/Documents/in/Business_Analytics?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29881262 coauthored" data-work_id="29881262" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29881262/Fuzzy_Rule_Based_Systems_for_Interpretable_Sentiment_Analysis">Fuzzy Rule Based Systems for Interpretable Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">—Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29881262" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">—Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the " white box " characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29881262" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7fcaf9e96b91cc1b15a2b134467e7c5b" rel="nofollow" data-download="{"attachment_id":50655018,"asset_id":29881262,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50655018/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3524561" href="https://cardiff.academia.edu/HanLiu">Han Liu</a><script data-card-contents-for-user="3524561" type="text/json">{"id":3524561,"first_name":"Han","last_name":"Liu","domain_name":"cardiff","page_name":"HanLiu","display_name":"Han Liu","profile_url":"https://cardiff.academia.edu/HanLiu?f_ri=5379","photo":"https://0.academia-photos.com/3524561/1209062/1513408/s65_han.liu.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-29881262">+1</span><div class="hidden js-additional-users-29881262"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://port.academia.edu/MihaelaCocea">Mihaela Cocea</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-29881262'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-29881262').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_29881262 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29881262"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29881262, container: ".js-paper-rank-work_29881262", }); });</script></li><li class="js-percentile-work_29881262 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29881262; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_29881262"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29881262 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29881262"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29881262; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29881262]").text(description); $(".js-view-count-work_29881262").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29881262").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29881262"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="982644" rel="nofollow" href="https://www.academia.edu/Documents/in/Fuzzy_Rule_based_System">Fuzzy Rule based System</a><script data-card-contents-for-ri="982644" type="text/json">{"id":982644,"name":"Fuzzy Rule based System","url":"https://www.academia.edu/Documents/in/Fuzzy_Rule_based_System?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29881262]'), work: {"id":29881262,"title":"Fuzzy Rule Based Systems for Interpretable Sentiment Analysis","created_at":"2016-11-16T03:21:57.590-08:00","url":"https://www.academia.edu/29881262/Fuzzy_Rule_Based_Systems_for_Interpretable_Sentiment_Analysis?f_ri=5379","dom_id":"work_29881262","summary":"—Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the \" white box \" characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.","downloadable_attachments":[{"id":50655018,"asset_id":29881262,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3524561,"first_name":"Han","last_name":"Liu","domain_name":"cardiff","page_name":"HanLiu","display_name":"Han Liu","profile_url":"https://cardiff.academia.edu/HanLiu?f_ri=5379","photo":"https://0.academia-photos.com/3524561/1209062/1513408/s65_han.liu.jpg"},{"id":31663136,"first_name":"Mihaela","last_name":"Cocea","domain_name":"port","page_name":"MihaelaCocea","display_name":"Mihaela Cocea","profile_url":"https://port.academia.edu/MihaelaCocea?f_ri=5379","photo":"https://0.academia-photos.com/31663136/9470372/10552222/s65_mihaela.cocea.png"}],"research_interests":[{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":982644,"name":"Fuzzy Rule based System","url":"https://www.academia.edu/Documents/in/Fuzzy_Rule_based_System?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14184718" data-work_id="14184718" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14184718/Negative_emotions_boost_user_activity_at_BBC_forum">Negative emotions boost user activity at BBC forum</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14184718" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users were investigated. Scale free distibutions were observed for activity in individual discussion threads as well as for overall activity. The number of unique users in a thread normalised by the thread length decays with thread length, suggesting that thread life is sustained by mutual discussions rather than by independent comments. Automatic sentiment analysis shows that most posts contain negative emotions and that the most active users in individual threads express predominantly negative sentiments. It follows that the average emotion of longer threads is more negative and that threads can be sustained by negative comments.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14184718" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="33674a23ab53a3ba3fbf6e98add9f7f0" rel="nofollow" data-download="{"attachment_id":44525994,"asset_id":14184718,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44525994/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33169764" href="https://independent.academia.edu/PawelSobkowicz">Pawel Sobkowicz</a><script data-card-contents-for-user="33169764" type="text/json">{"id":33169764,"first_name":"Pawel","last_name":"Sobkowicz","domain_name":"independent","page_name":"PawelSobkowicz","display_name":"Pawel Sobkowicz","profile_url":"https://independent.academia.edu/PawelSobkowicz?f_ri=5379","photo":"https://0.academia-photos.com/33169764/121553131/110886771/s65_pawel.sobkowicz.png"}</script></span></span></li><li class="js-paper-rank-work_14184718 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14184718"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14184718, container: ".js-paper-rank-work_14184718", }); });</script></li><li class="js-percentile-work_14184718 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 14184718; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_14184718"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_14184718 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="14184718"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 14184718; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=14184718]").text(description); $(".js-view-count-work_14184718").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14184718").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14184718"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="318" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Physics">Mathematical Physics</a>, <script data-card-contents-for-ri="318" type="text/json">{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="518" rel="nofollow" href="https://www.academia.edu/Documents/in/Quantum_Physics">Quantum Physics</a>, <script data-card-contents-for-ri="518" type="text/json">{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="42162" rel="nofollow" href="https://www.academia.edu/Documents/in/Emotions">Emotions</a><script data-card-contents-for-ri="42162" type="text/json">{"id":42162,"name":"Emotions","url":"https://www.academia.edu/Documents/in/Emotions?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14184718]'), work: {"id":14184718,"title":"Negative emotions boost user activity at BBC forum","created_at":"2015-07-19T02:24:44.837-07:00","url":"https://www.academia.edu/14184718/Negative_emotions_boost_user_activity_at_BBC_forum?f_ri=5379","dom_id":"work_14184718","summary":"We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users were investigated. Scale free distibutions were observed for activity in individual discussion threads as well as for overall activity. The number of unique users in a thread normalised by the thread length decays with thread length, suggesting that thread life is sustained by mutual discussions rather than by independent comments. Automatic sentiment analysis shows that most posts contain negative emotions and that the most active users in individual threads express predominantly negative sentiments. It follows that the average emotion of longer threads is more negative and that threads can be sustained by negative comments.","downloadable_attachments":[{"id":44525994,"asset_id":14184718,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33169764,"first_name":"Pawel","last_name":"Sobkowicz","domain_name":"independent","page_name":"PawelSobkowicz","display_name":"Pawel Sobkowicz","profile_url":"https://independent.academia.edu/PawelSobkowicz?f_ri=5379","photo":"https://0.academia-photos.com/33169764/121553131/110886771/s65_pawel.sobkowicz.png"}],"research_interests":[{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=5379","nofollow":true},{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":42162,"name":"Emotions","url":"https://www.academia.edu/Documents/in/Emotions?f_ri=5379","nofollow":true},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=5379"},{"id":92702,"name":"Scale-Free Networks","url":"https://www.academia.edu/Documents/in/Scale-Free_Networks?f_ri=5379"},{"id":219474,"name":"Empirical Study","url":"https://www.academia.edu/Documents/in/Empirical_Study?f_ri=5379"},{"id":249843,"name":"Agent Modeling","url":"https://www.academia.edu/Documents/in/Agent_Modeling?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13305246" data-work_id="13305246" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13305246/Supervised_and_Traditional_Term_Weighting_Methods_for_Sentiment_Analysis">Supervised and Traditional Term Weighting Methods for Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Özetçe-Duygu analizi bir metin sınıflandırma problemi olup popülerliği ve ticari getirileri sebebiyle günümüzde üzerinde çokça çalışılan bir konudur. Metin sınıflandırmadaki en önemli nokta metinlerin nasıl temsil edilmesi gerektiğidir.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13305246" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Özetçe-Duygu analizi bir metin sınıflandırma problemi olup popülerliği ve ticari getirileri sebebiyle günümüzde üzerinde çokça çalışılan bir konudur. Metin sınıflandırmadaki en önemli nokta metinlerin nasıl temsil edilmesi gerektiğidir. Geleneksel eğiticisiz yöntemler yerine terimlerin sınıf dağılımlarını da hesaba katan eğiticili yöntemler literatürde sıklıkla kullanılmaya başlanmıştır. Bu çalışmada Türkçe Twitter gönderilerinden oluşan 2 veri kümesi üzerinde bu yöntemler çeşitli boyutlarda karşılaştırılmıştır. Sonuç olarak eğiticili yöntemlerin daha başarılı ve daha uygulanabilir oldukları görülmüştür.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13305246" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3ee1d9dba48f087928f5c19b6a2876c9" rel="nofollow" data-download="{"attachment_id":38009920,"asset_id":13305246,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38009920/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="300872" href="https://yildiz.academia.edu/MehmetFatihAmasyali">Mehmet Fatih Amasyali</a><script data-card-contents-for-user="300872" type="text/json">{"id":300872,"first_name":"Mehmet Fatih","last_name":"Amasyali","domain_name":"yildiz","page_name":"MehmetFatihAmasyali","display_name":"Mehmet Fatih Amasyali","profile_url":"https://yildiz.academia.edu/MehmetFatihAmasyali?f_ri=5379","photo":"https://0.academia-photos.com/300872/60742/64548/s65_mehmet_fatih.amasyali.jpg"}</script></span></span></li><li class="js-paper-rank-work_13305246 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13305246"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13305246, container: ".js-paper-rank-work_13305246", }); });</script></li><li class="js-percentile-work_13305246 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13305246; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_13305246"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_13305246 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="13305246"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13305246; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13305246]").text(description); $(".js-view-count-work_13305246").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_13305246").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="13305246"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13305246]'), work: {"id":13305246,"title":"Supervised and Traditional Term Weighting Methods for Sentiment Analysis","created_at":"2015-06-26T01:30:51.128-07:00","url":"https://www.academia.edu/13305246/Supervised_and_Traditional_Term_Weighting_Methods_for_Sentiment_Analysis?f_ri=5379","dom_id":"work_13305246","summary":"Özetçe-Duygu analizi bir metin sınıflandırma problemi olup popülerliği ve ticari getirileri sebebiyle günümüzde üzerinde çokça çalışılan bir konudur. Metin sınıflandırmadaki en önemli nokta metinlerin nasıl temsil edilmesi gerektiğidir. Geleneksel eğiticisiz yöntemler yerine terimlerin sınıf dağılımlarını da hesaba katan eğiticili yöntemler literatürde sıklıkla kullanılmaya başlanmıştır. Bu çalışmada Türkçe Twitter gönderilerinden oluşan 2 veri kümesi üzerinde bu yöntemler çeşitli boyutlarda karşılaştırılmıştır. Sonuç olarak eğiticili yöntemlerin daha başarılı ve daha uygulanabilir oldukları görülmüştür.","downloadable_attachments":[{"id":38009920,"asset_id":13305246,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":300872,"first_name":"Mehmet Fatih","last_name":"Amasyali","domain_name":"yildiz","page_name":"MehmetFatihAmasyali","display_name":"Mehmet Fatih Amasyali","profile_url":"https://yildiz.academia.edu/MehmetFatihAmasyali?f_ri=5379","photo":"https://0.academia-photos.com/300872/60742/64548/s65_mehmet_fatih.amasyali.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35480862" data-work_id="35480862" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35480862/Enhancing_Social_Customer_Relationship_Management_by_Using_Sentiment_Analysis">Enhancing Social Customer Relationship Management by Using Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">These days most people use social media sites like Facebook, Twitter, etc. to review, buying and complain about products or services. According to the previous, most companies changed from traditional CRM to SCRM to be able to retain the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35480862" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">These days most people use social media sites like Facebook, Twitter, etc. to review, buying and complain about products or services. According to the previous, most companies changed from traditional CRM to SCRM to be able to retain the current Customers and also can compete with the others and get new Customers. Starting from the importance of Customer reviews about products or services for companies, we started working on this paper. Sentiment analysis model was used to get Customers opinions about product or service then manual analysis has been done on negative and positive reviews. The result of this research is beneficial reports for business decision makers to enhance SCRM.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35480862" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="224dabb6fb03b332335ff431810dabba" rel="nofollow" data-download="{"attachment_id":55343385,"asset_id":35480862,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55343385/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="105152" href="https://helwan.academia.edu/DrMonaNasr">Prof. Mona Nasr</a><script data-card-contents-for-user="105152" type="text/json">{"id":105152,"first_name":"Prof. Mona","last_name":"Nasr","domain_name":"helwan","page_name":"DrMonaNasr","display_name":"Prof. Mona Nasr","profile_url":"https://helwan.academia.edu/DrMonaNasr?f_ri=5379","photo":"https://0.academia-photos.com/105152/28938/48849254/s65_prof._mona.nasr.jpg"}</script></span></span></li><li class="js-paper-rank-work_35480862 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35480862"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35480862, container: ".js-paper-rank-work_35480862", }); });</script></li><li class="js-percentile-work_35480862 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35480862; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_35480862"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_35480862 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="35480862"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35480862; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35480862]").text(description); $(".js-view-count-work_35480862").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35480862").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35480862"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13222" rel="nofollow" href="https://www.academia.edu/Documents/in/Customer_Relationship_Management_CRM_">Customer Relationship Management (CRM)</a>, <script data-card-contents-for-ri="13222" type="text/json">{"id":13222,"name":"Customer Relationship Management (CRM)","url":"https://www.academia.edu/Documents/in/Customer_Relationship_Management_CRM_?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1591473" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Customer_Relationship_Management">Social Customer Relationship Management</a><script data-card-contents-for-ri="1591473" type="text/json">{"id":1591473,"name":"Social Customer Relationship Management","url":"https://www.academia.edu/Documents/in/Social_Customer_Relationship_Management?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35480862]'), work: {"id":35480862,"title":"Enhancing Social Customer Relationship Management by Using Sentiment Analysis","created_at":"2017-12-20T14:51:16.232-08:00","url":"https://www.academia.edu/35480862/Enhancing_Social_Customer_Relationship_Management_by_Using_Sentiment_Analysis?f_ri=5379","dom_id":"work_35480862","summary":"These days most people use social media sites like Facebook, Twitter, etc. to review, buying and complain about products or services. According to the previous, most companies changed from traditional CRM to SCRM to be able to retain the current Customers and also can compete with the others and get new Customers. Starting from the importance of Customer reviews about products or services for companies, we started working on this paper. Sentiment analysis model was used to get Customers opinions about product or service then manual analysis has been done on negative and positive reviews. The result of this research is beneficial reports for business decision makers to enhance SCRM. ","downloadable_attachments":[{"id":55343385,"asset_id":35480862,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":105152,"first_name":"Prof. Mona","last_name":"Nasr","domain_name":"helwan","page_name":"DrMonaNasr","display_name":"Prof. Mona Nasr","profile_url":"https://helwan.academia.edu/DrMonaNasr?f_ri=5379","photo":"https://0.academia-photos.com/105152/28938/48849254/s65_prof._mona.nasr.jpg"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379","nofollow":true},{"id":13222,"name":"Customer Relationship Management (CRM)","url":"https://www.academia.edu/Documents/in/Customer_Relationship_Management_CRM_?f_ri=5379","nofollow":true},{"id":1591473,"name":"Social Customer Relationship Management","url":"https://www.academia.edu/Documents/in/Social_Customer_Relationship_Management?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67335568" data-work_id="67335568" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67335568/Opinion_mining_of_movie_review_using_hybrid_method_of_support_vector_machine_and_particle_swarm_optimization">Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67335568" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67335568" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="631fdb715ce9202f224ff5c30b8242eb" rel="nofollow" data-download="{"attachment_id":78191158,"asset_id":67335568,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78191158/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="157801475" href="https://independent.academia.edu/PramudyaAnanta11">Pramudya Ananta</a><script data-card-contents-for-user="157801475" type="text/json">{"id":157801475,"first_name":"Pramudya","last_name":"Ananta","domain_name":"independent","page_name":"PramudyaAnanta11","display_name":"Pramudya Ananta","profile_url":"https://independent.academia.edu/PramudyaAnanta11?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_67335568 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67335568"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67335568, container: ".js-paper-rank-work_67335568", }); });</script></li><li class="js-percentile-work_67335568 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67335568; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_67335568"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_67335568 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="67335568"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67335568; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67335568]").text(description); $(".js-view-count-work_67335568").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67335568").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67335568"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="464" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Retrieval">Information Retrieval</a>, <script data-card-contents-for-ri="464" type="text/json">{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67335568]'), work: {"id":67335568,"title":"Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization","created_at":"2022-01-06T00:45:32.021-08:00","url":"https://www.academia.edu/67335568/Opinion_mining_of_movie_review_using_hybrid_method_of_support_vector_machine_and_particle_swarm_optimization?f_ri=5379","dom_id":"work_67335568","summary":"Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%.","downloadable_attachments":[{"id":78191158,"asset_id":67335568,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":157801475,"first_name":"Pramudya","last_name":"Ananta","domain_name":"independent","page_name":"PramudyaAnanta11","display_name":"Pramudya Ananta","profile_url":"https://independent.academia.edu/PramudyaAnanta11?f_ri=5379","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=5379"},{"id":51168,"name":"Predictive Analytics","url":"https://www.academia.edu/Documents/in/Predictive_Analytics?f_ri=5379"},{"id":79948,"name":"Web Programming","url":"https://www.academia.edu/Documents/in/Web_Programming?f_ri=5379"},{"id":126300,"name":"Big Data","url":"https://www.academia.edu/Documents/in/Big_Data?f_ri=5379"},{"id":201685,"name":"Opinion Mining","url":"https://www.academia.edu/Documents/in/Opinion_Mining?f_ri=5379"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm?f_ri=5379"},{"id":389029,"name":"Opinion","url":"https://www.academia.edu/Documents/in/Opinion?f_ri=5379"},{"id":863644,"name":"Sentiment","url":"https://www.academia.edu/Documents/in/Sentiment?f_ri=5379"},{"id":1009312,"name":"Geographic Information Systems (GIS)","url":"https://www.academia.edu/Documents/in/Geographic_Information_Systems_GIS_?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_44978369" data-work_id="44978369" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44978369/Sentiment_Dynamics_in_Social_Media_News_Channels">Sentiment Dynamics in Social Media News Channels</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, all the traditional news channels are using social media to catch the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44978369" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, all the traditional news channels are using social media to catch the attention of users. Each news channel has its own strategy to attract more users. In this paper, we analyze how the news channels use sentiment to garner users' attention in social media. We compare the sentiment of news posts generated by television, radio and print media, to show the di erences in the news covered by these channels. We also analyze users' reactions and sentiment of users' opinions on news posts with di erent sentiments. We do our analysis on the dataset extracted from the Facebook Pages of ve popular news channels. Our dataset contains 0.15 million news posts and 1.13 billion users reactions. Our result shows that sentiment of the user opinion strongly correlates with the sentiment of news posts and the type of information source. Our study also illustrates the di erences between the social media news channels of di erent types of news sources.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44978369" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3c6c0f2faf2a993f4b7cc82450b6a95d" rel="nofollow" data-download="{"attachment_id":65516203,"asset_id":44978369,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65516203/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22463572" href="https://iiti.academia.edu/NagendraKumar">Nagendra Kumar</a><script data-card-contents-for-user="22463572" type="text/json">{"id":22463572,"first_name":"Nagendra","last_name":"Kumar","domain_name":"iiti","page_name":"NagendraKumar","display_name":"Nagendra Kumar","profile_url":"https://iiti.academia.edu/NagendraKumar?f_ri=5379","photo":"https://0.academia-photos.com/22463572/19102237/42205923/s65_nagendra.kumar.jpg"}</script></span></span></li><li class="js-paper-rank-work_44978369 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44978369"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44978369, container: ".js-paper-rank-work_44978369", }); });</script></li><li class="js-percentile-work_44978369 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 44978369; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_44978369"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_44978369 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="44978369"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44978369; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=44978369]").text(description); $(".js-view-count-work_44978369").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_44978369").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="44978369"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="491" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Technology">Information Technology</a>, <script data-card-contents-for-ri="491" type="text/json">{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2553" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Networking">Social Networking</a>, <script data-card-contents-for-ri="2553" type="text/json">{"id":2553,"name":"Social Networking","url":"https://www.academia.edu/Documents/in/Social_Networking?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=44978369]'), work: {"id":44978369,"title":"Sentiment Dynamics in Social Media News Channels","created_at":"2021-01-25T22:22:38.941-08:00","url":"https://www.academia.edu/44978369/Sentiment_Dynamics_in_Social_Media_News_Channels?f_ri=5379","dom_id":"work_44978369","summary":"Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, all the traditional news channels are using social media to catch the attention of users. Each news channel has its own strategy to attract more users. In this paper, we analyze how the news channels use sentiment to garner users' attention in social media. We compare the sentiment of news posts generated by television, radio and print media, to show the di erences in the news covered by these channels. We also analyze users' reactions and sentiment of users' opinions on news posts with di erent sentiments. We do our analysis on the dataset extracted from the Facebook Pages of ve popular news channels. Our dataset contains 0.15 million news posts and 1.13 billion users reactions. Our result shows that sentiment of the user opinion strongly correlates with the sentiment of news posts and the type of information source. Our study also illustrates the di erences between the social media news channels of di erent types of news sources.","downloadable_attachments":[{"id":65516203,"asset_id":44978369,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":22463572,"first_name":"Nagendra","last_name":"Kumar","domain_name":"iiti","page_name":"NagendraKumar","display_name":"Nagendra Kumar","profile_url":"https://iiti.academia.edu/NagendraKumar?f_ri=5379","photo":"https://0.academia-photos.com/22463572/19102237/42205923/s65_nagendra.kumar.jpg"}],"research_interests":[{"id":491,"name":"Information Technology","url":"https://www.academia.edu/Documents/in/Information_Technology?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":2553,"name":"Social Networking","url":"https://www.academia.edu/Documents/in/Social_Networking?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379"},{"id":69100,"name":"Data Science","url":"https://www.academia.edu/Documents/in/Data_Science?f_ri=5379"},{"id":145307,"name":"New Media, Social Network Analysis, e-research, Link analysis, Social Network Sites, Twitter, Facebook, Political Communication","url":"https://www.academia.edu/Documents/in/New_Media_Social_Network_Analysis_e-research_Link_analysis_Social_Network_Sites_Twitter_Facebo?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43294287 coauthored" data-work_id="43294287" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43294287/Sentiment_Analysis_of_Events_in_Social_Media">Sentiment Analysis of Events in Social Media</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The growing popularity of Online Social Networks has open new research directions and perspectives for content analysis, i.e., Network Analysis and Natural Language Processing. From the perspective of information spread, the Network... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43294287" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The growing popularity of Online Social Networks has open new research directions and perspectives for content analysis, i.e., Network Analysis and Natural Language Processing. From the perspective of information spread, the Network Analysis community propose Event Detection. This approach focuses on the network features, without an in-depth analysis of the textual content, summarization being a preferred method. Natural Language Processing analyses only the textual content, not integrating the graph-based structure of the network. To address these limitations, we propose a method that bridges the two directions and integrates content-awareness into network-awareness. Our method uses event detection to extract topics of interest and then applies sentiment analysis on each event. The obtained results have high accuracy, proving that our method determines with high precision the overall sentiment of the detected events.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43294287" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0180d0c013103dc66d84850ea5fb7885" rel="nofollow" data-download="{"attachment_id":63571488,"asset_id":43294287,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63571488/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="161359756" href="https://independent.academia.edu/AlexandruPetrescu12">Alexandru Petrescu</a><script data-card-contents-for-user="161359756" type="text/json">{"id":161359756,"first_name":"Alexandru","last_name":"Petrescu","domain_name":"independent","page_name":"AlexandruPetrescu12","display_name":"Alexandru Petrescu","profile_url":"https://independent.academia.edu/AlexandruPetrescu12?f_ri=5379","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-43294287">+2</span><div class="hidden js-additional-users-43294287"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://pub.academia.edu/CiprianOctavianTruic%C4%83">Ciprian-Octavian Truică</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://pub.academia.edu/EApostol">Elena-Simona APOSTOL</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-43294287'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-43294287').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_43294287 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43294287"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43294287, container: ".js-paper-rank-work_43294287", }); });</script></li><li class="js-percentile-work_43294287 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43294287; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_43294287"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_43294287 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="43294287"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43294287; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43294287]").text(description); $(".js-view-count-work_43294287").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43294287").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43294287"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a>, <script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49341" rel="nofollow" href="https://www.academia.edu/Documents/in/Event_Detection">Event Detection</a><script data-card-contents-for-ri="49341" type="text/json">{"id":49341,"name":"Event Detection","url":"https://www.academia.edu/Documents/in/Event_Detection?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43294287]'), work: {"id":43294287,"title":"Sentiment Analysis of Events in Social Media","created_at":"2020-06-09T01:03:50.225-07:00","url":"https://www.academia.edu/43294287/Sentiment_Analysis_of_Events_in_Social_Media?f_ri=5379","dom_id":"work_43294287","summary":"The growing popularity of Online Social Networks has open new research directions and perspectives for content analysis, i.e., Network Analysis and Natural Language Processing. From the perspective of information spread, the Network Analysis community propose Event Detection. This approach focuses on the network features, without an in-depth analysis of the textual content, summarization being a preferred method. Natural Language Processing analyses only the textual content, not integrating the graph-based structure of the network. To address these limitations, we propose a method that bridges the two directions and integrates content-awareness into network-awareness. Our method uses event detection to extract topics of interest and then applies sentiment analysis on each event. The obtained results have high accuracy, proving that our method determines with high precision the overall sentiment of the detected events.","downloadable_attachments":[{"id":63571488,"asset_id":43294287,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":161359756,"first_name":"Alexandru","last_name":"Petrescu","domain_name":"independent","page_name":"AlexandruPetrescu12","display_name":"Alexandru Petrescu","profile_url":"https://independent.academia.edu/AlexandruPetrescu12?f_ri=5379","photo":"/images/s65_no_pic.png"},{"id":27824967,"first_name":"Ciprian-Octavian","last_name":"Truică","domain_name":"pub","page_name":"CiprianOctavianTruică","display_name":"Ciprian-Octavian Truică","profile_url":"https://pub.academia.edu/CiprianOctavianTruic%C4%83?f_ri=5379","photo":"https://0.academia-photos.com/27824967/7898836/35244750/s65_ciprian-octavian.truic_.jpg"},{"id":24750943,"first_name":"Elena-Simona","last_name":"APOSTOL","domain_name":"pub","page_name":"EApostol","display_name":"Elena-Simona APOSTOL","profile_url":"https://pub.academia.edu/EApostol?f_ri=5379","photo":"https://0.academia-photos.com/24750943/45018637/120078085/s65_elena-simona.apostol.png"}],"research_interests":[{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":49341,"name":"Event Detection","url":"https://www.academia.edu/Documents/in/Event_Detection?f_ri=5379","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75505530" data-work_id="75505530" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75505530/Combination_of_domain_knowledge_and_deep_learning_for_sentiment_analysis_of_short_and_informal_messages_on_social_media">Combination of domain knowledge and deep learning for sentiment analysis of short and informal messages on social media</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75505530" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75505530" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a7ddaac3f4806bb5ec63e773e2b76f8a" rel="nofollow" data-download="{"attachment_id":83247441,"asset_id":75505530,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83247441/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="153910132" href="https://independent.academia.edu/tringuyen503">tri nguyen</a><script data-card-contents-for-user="153910132" type="text/json">{"id":153910132,"first_name":"tri","last_name":"nguyen","domain_name":"independent","page_name":"tringuyen503","display_name":"tri nguyen","profile_url":"https://independent.academia.edu/tringuyen503?f_ri=5379","photo":"https://0.academia-photos.com/153910132/149874434/139448510/s65_tri.nguyen.jpeg"}</script></span></span></li><li class="js-paper-rank-work_75505530 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75505530"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75505530, container: ".js-paper-rank-work_75505530", }); 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$(".js-view-count[data-work-id=75505530]").text(description); $(".js-view-count-work_75505530").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75505530").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75505530"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75505530]'), work: {"id":75505530,"title":"Combination of domain knowledge and deep learning for sentiment analysis of short and informal messages on social media","created_at":"2022-04-05T02:02:09.212-07:00","url":"https://www.academia.edu/75505530/Combination_of_domain_knowledge_and_deep_learning_for_sentiment_analysis_of_short_and_informal_messages_on_social_media?f_ri=5379","dom_id":"work_75505530","summary":"Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.","downloadable_attachments":[{"id":83247441,"asset_id":75505530,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":153910132,"first_name":"tri","last_name":"nguyen","domain_name":"independent","page_name":"tringuyen503","display_name":"tri nguyen","profile_url":"https://independent.academia.edu/tringuyen503?f_ri=5379","photo":"https://0.academia-photos.com/153910132/149874434/139448510/s65_tri.nguyen.jpeg"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=5379","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=5379","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48549395" data-work_id="48549395" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48549395/Sentiment_in_short_strength_detection_informal_text">Sentiment in short strength detection informal text</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48549395" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48549395" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="507df7950e79be1764f62a61732f28ce" rel="nofollow" data-download="{"attachment_id":67103318,"asset_id":48549395,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67103318/download_file?st=MTc0MDYwNTUzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1072775" href="https://constructor.academia.edu/ArvidKappas">Arvid Kappas</a><script data-card-contents-for-user="1072775" type="text/json">{"id":1072775,"first_name":"Arvid","last_name":"Kappas","domain_name":"constructor","page_name":"ArvidKappas","display_name":"Arvid Kappas","profile_url":"https://constructor.academia.edu/ArvidKappas?f_ri=5379","photo":"https://0.academia-photos.com/1072775/371220/449813/s65_arvid.kappas.jpg"}</script></span></span></li><li class="js-paper-rank-work_48549395 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48549395"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48549395, container: ".js-paper-rank-work_48549395", }); 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$(".js-view-count[data-work-id=48549395]").text(description); $(".js-view-count-work_48549395").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48549395").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48549395"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="37" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5379" rel="nofollow" href="https://www.academia.edu/Documents/in/Sentiment_Analysis">Sentiment Analysis</a><script data-card-contents-for-ri="5379" type="text/json">{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48549395]'), work: {"id":48549395,"title":"Sentiment in short strength detection informal text","created_at":"2021-05-04T22:57:08.478-07:00","url":"https://www.academia.edu/48549395/Sentiment_in_short_strength_detection_informal_text?f_ri=5379","dom_id":"work_48549395","summary":"A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.","downloadable_attachments":[{"id":67103318,"asset_id":48549395,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1072775,"first_name":"Arvid","last_name":"Kappas","domain_name":"constructor","page_name":"ArvidKappas","display_name":"Arvid Kappas","profile_url":"https://constructor.academia.edu/ArvidKappas?f_ri=5379","photo":"https://0.academia-photos.com/1072775/371220/449813/s65_arvid.kappas.jpg"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=5379","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=5379","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=5379","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=5379","nofollow":true},{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction?f_ri=5379"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=5379"},{"id":50238,"name":"Affect","url":"https://www.academia.edu/Documents/in/Affect?f_ri=5379"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies?f_ri=5379"},{"id":161176,"name":"The","url":"https://www.academia.edu/Documents/in/The?f_ri=5379"},{"id":1863718,"name":"The American","url":"https://www.academia.edu/Documents/in/The_American?f_ri=5379"},{"id":2213585,"name":"Information System","url":"https://www.academia.edu/Documents/in/Information_System?f_ri=5379"}]}, }) } })();</script></ul></li></ul></div></div></div><div class="u-taCenter Pagination"><ul class="pagination"><li 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