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detection of tweets. We have focused on unstructured and noisy text like tweets on which linguistic tools like parsers and POS-taggers don't work properly. We have showed how conjunctions, connectives, modals and conditionals affect the sentiments in tweets. We have also handled the commonly used abbreviations, slangs and collocations which are usually used in short text messages like tweets. This work focuses on a Web based application which produces results in real time. 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class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="90303201" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/90303201/Sielers_Feature_analysis_and_polarity_classification_of_expressions_from_Twitter_and_SMS_data">Sielers: Feature analysis and polarity classification of expressions from Twitter and SMS data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="110897676" href="https://srmuniv.academia.edu/HJain">Harshit Jain</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Sielers: Feature analysis and polarity classification of expressions from Twitter and SMS 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href="https://www.academia.edu/43903898/IJERT_Sentiment_Analysis_Detecting_Polarity_in_Text">IJERT-Sentiment Analysis Detecting Polarity in Text</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="109571315" href="https://independent.academia.edu/IJERTORG">IJERT Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Research and Technology (IJERT), 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">https://www.ijert.org/sentiment-analysis-detecting-polarity-in-text https://www.ijert.org/research/sentiment-analysis-detecting-polarity-in-text-IJERTCONV8IS11016.pdf we can see that now a days there is raise of social media, which in turn have changed the perspective in every field such as networking, socialization and personalization. People are using these data for different purpose like predicting the results of the election, communication, business, and marketing and in many more fields. Here extracting the valuable information that is from twitter which is a social media platform and then analyzing the tweets and then classifying those tweets into different polarity like positive, negative and neutral. And yes the data extracted can be from any of the domain which is specified. The application proposed here facilitates the extraction of some keywords, entities, synonyms and parts of speech that will be used in tweets and this will be used to segregate or classify the tweets that are data and perform sentiment analysis. This project is mainly on twitter data abstracting but we can also do this polarity check for other applications also like for example Facebook , Instagram.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"IJERT-Sentiment Analysis Detecting Polarity in Text","attachmentId":64227955,"attachmentType":"pdf","work_url":"https://www.academia.edu/43903898/IJERT_Sentiment_Analysis_Detecting_Polarity_in_Text","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43903898/IJERT_Sentiment_Analysis_Detecting_Polarity_in_Text"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="46850480" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/46850480/Polarity_Testing_and_Analysis_of_tweets_in_Twitter_using_Tweepy">Polarity Testing and Analysis of tweets in Twitter using Tweepy</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="124363894" href="https://stanford.academia.edu/RahulPandya">Clara Pandya</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="189348790" href="https://independent.academia.edu/SujalCharak">Sujal Charak</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computational Research, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Sentiment Analysis has been a very important part of analytics for data scientists over the years. It has been a very detailed and an important area of research and development which enables the user to find the acknowledgement factor for the area of interest. Social media is always evolving and the most interactive media of individual communication and broadcasting. Sentimental analysis of is the best alternative for peer reviewing in terms of a certain criterion. This paper deals with an analytic study over a twitter based dataset which involves pulling of certain number of tweets using API linking and then performing the polarity check on the number of tweets pulled with respect to that particular keyword. An approach involving unsupervised machine learning algorithms along with natural language processing generates significant results in the task over the traditional lexicon method used.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Polarity Testing and Analysis of tweets in Twitter using Tweepy","attachmentId":66263042,"attachmentType":"pdf","work_url":"https://www.academia.edu/46850480/Polarity_Testing_and_Analysis_of_tweets_in_Twitter_using_Tweepy","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/46850480/Polarity_Testing_and_Analysis_of_tweets_in_Twitter_using_Tweepy"><span 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class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages","attachmentId":91379570,"attachmentType":"pdf","work_url":"https://www.academia.edu/87062582/On_the_usefulness_of_lexical_and_syntactic_processing_in_polarity_classification_of_Twitter_messages","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87062582/On_the_usefulness_of_lexical_and_syntactic_processing_in_polarity_classification_of_Twitter_messages"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="30013361" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/30013361/Modelling_Public_Sentiment_in_Twitter_Using_Linguistic_Patterns_to_Enhance_Supervised_Learning">Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35873" href="https://ipn.academia.edu/AlexanderGelbukh">Alexander Gelbukh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science, 2015</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" 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data-collection-position="6" data-entity-id="73095773" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/73095773/GTI_An_Unsupervised_Approach_for_Sentiment_Analysis_in_Twitter">GTI: An Unsupervised Approach for Sentiment Analysis in Twitter</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="25810516" href="https://independent.academia.edu/MilagrosFern%C3%A1ndezGavilanes">Milagros Fernández Gavilanes</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"GTI: An Unsupervised Approach for Sentiment Analysis in Twitter","attachmentId":81752076,"attachmentType":"pdf","work_url":"https://www.academia.edu/73095773/GTI_An_Unsupervised_Approach_for_Sentiment_Analysis_in_Twitter","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/73095773/GTI_An_Unsupervised_Approach_for_Sentiment_Analysis_in_Twitter"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="70395740" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/70395740/Polarity_analysis_of_non_figurative_tweets_Tw_StAR_participation_on_DEFT_2017">Polarity analysis of non figurative tweets : Tw-StAR participation on DEFT 2017</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37940137" href="https://selcuk.academia.edu/halamulki">hala mulki</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we present our contribution in DEFT 2017 international workshop. We have tackled task 1 entitled “Polarity analysis of non figurative tweets ”. We propose three sentiment classification models implemented using lexicon-based, supervised, and document embedding-based methods. For the first model, a novel strategy is introduced where Named Entities (NEs) have been involved in the Sentiment Analysis task. The first two models adopted bag-of-N-grams features while for the third model, features have been extracted automatically from the data itself in the form of document vectors. The official evaluation of the three models indicated that the best performance was achieved by the supervised learning-based model. Nevertheless, the results obtained by the document embeddingbased model are considered promising and can be further improved if pretrianed French word vectors are used to initialize the model’s features. MOTS-CLÉS : Analyse des sentiments, apprentissage supervisé, m...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Polarity analysis of non figurative tweets : Tw-StAR participation on DEFT 2017","attachmentId":80166705,"attachmentType":"pdf","work_url":"https://www.academia.edu/70395740/Polarity_analysis_of_non_figurative_tweets_Tw_StAR_participation_on_DEFT_2017","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/70395740/Polarity_analysis_of_non_figurative_tweets_Tw_StAR_participation_on_DEFT_2017"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="29778745" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/29778745/Automatic_Unsupervised_Polarity_Detection_on_a_Twitter_Data_Stream">Automatic Unsupervised Polarity Detection on a Twitter Data Stream</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="186125" href="https://cnr-it.academia.edu/AgneseAugello">Agnese Augello</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014 IEEE International Conference on Semantic Computing, 2014</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Automatic Unsupervised Polarity Detection on a Twitter Data Stream","attachmentId":50231271,"attachmentType":"pdf","work_url":"https://www.academia.edu/29778745/Automatic_Unsupervised_Polarity_Detection_on_a_Twitter_Data_Stream","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/29778745/Automatic_Unsupervised_Polarity_Detection_on_a_Twitter_Data_Stream"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="44816373" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44816373/Developing_an_Approach_to_Evaluate_and_Observe_Sentiments_of_Tweets">Developing an Approach to Evaluate and Observe Sentiments of Tweets</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64525554" href="https://technoscienceacademy.academia.edu/IJSRCSEIT">International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Social media are computer- mediated tools that allow people or companies to create, share or exchange information, career interests, ideas and the form of text, audio, video, image in virtual communities and networks. Twitter is a trendy microblogging service where many users procreate various status messages called tweets. Twitter is the fastest way to get real time information from the around the world. Tweets themselves are short and compact, like newspaper headlines. Analysis of sentiment is widely observed on numerous social networking websites. Nowadays, microblogging sites deed as a base to perceive the actual social opinion. The task of sentimental analysis is often known by many other names as opinion extraction, opinion mining, sentiment mining, subjectivity analysis.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Developing an Approach to Evaluate and Observe Sentiments of Tweets","attachmentId":65316447,"attachmentType":"pdf","work_url":"https://www.academia.edu/44816373/Developing_an_Approach_to_Evaluate_and_Observe_Sentiments_of_Tweets","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44816373/Developing_an_Approach_to_Evaluate_and_Observe_Sentiments_of_Tweets"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":95799940,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":95799940,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_95799940" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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