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(PDF) " Opinion Mining in Twitter – Sarcasm Detection "
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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":35085669,"created_at":"2017-11-08T01:39:42.220-08:00","from_world_paper_id":null,"updated_at":"2024-11-15T10:17:39.635-08:00","_data":{"grobid_abstract":"Sarcasm is largely used in social networks and micro blogging websites, where people mock or criticize in a way that makes it difficult even for humans to tell if what is said is what is meant. To recognize sarcastic statements can be very useful when it comes to improving automatic sentiment analysis the data collected from social networks. This work demonstrated the importance of detecting sarcastic tweets automatically, and also demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not. In this work we propose a method to detect sarcasm in Twitter that makes use of the different components of the tweet. Work proposes four categories of features that cover different types of sarcasm we defined, and that will be used to classify tweets into sarcastic and non-sarcastic. To evaluate the performances of our work study the importance of each of the proposed sets of features and evaluate its added value to the classification.","grobid_abstract_attachment_id":"54946723"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"\" Opinion Mining in Twitter – Sarcasm Detection \"","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [31493941]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":54946723,"attachmentType":"pdf"}"><img alt="First page of “" Opinion Mining in Twitter – Sarcasm Detection "”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/54946723/mini_magick20190115-10092-320q3e.png?1547586036" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">" Opinion Mining in Twitter – Sarcasm Detection "</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET"><img alt="Profile image of IRJET Journal" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/31493941/9304077/11813823/s65_irjet.journal.jpg" />IRJET Journal</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">4 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 35085669; 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There is no use of online information if we cannot extract it and use it to cater our ventures. In order to frame up any summary, it is required to find the relevant text with complete omission of unnecessary information while keeping the focus on details and compile them into a document. The sentiment analysis is the approach used to evaluate users ’ sentiments on websites, forums, comments, feedback as negative, positive or neutral. But, sometimes, people express their negative sentiment in a positive manner. This flips the polarity of the sentence and sentiment analysis performance is affected. Thus, detection of sarcasm is an important part of sentiment analysis. Input data features are extracted and data needs to be classified as sarcastic or not. To increase accuracy for the sarcasm detection from twitter data new features needs to add fort t...</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":"An Approach to Detect Sarcasm in Tweets","attachmentId":77396798,"attachmentType":"pdf","work_url":"https://www.academia.edu/66055588/An_Approach_to_Detect_Sarcasm_in_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/66055588/An_Approach_to_Detect_Sarcasm_in_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 class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="45602559" 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/45602559/IMPLEMENTATION_OF_SARCASM_DETECTION_ON_TWITTER_DATA">IMPLEMENTATION OF SARCASM DETECTION ON TWITTER 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="129887699" href="https://independent.academia.edu/irjmets">International Research Journal of Modernization in Engineering Technology and Science (IRJMETS)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJMETS Publication, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">One of the hot topics of recent years is analyzing or extracting microblog data. Analysis of feelings is one of the micro-blog data analysis techniques. Analysis of feelings refers to Identification of online courage and opinion with regard to an explicit theme or product. Sarcasm is one of the most frequently used ironies in micro-blogs or social network sites. Sarcasm is another way of transmitting information from person to person. It could be used in different ways, such as mockery of somebody. One of the key principles for enhancing data analysis and developing automatic feeling analytics is sarcasm detection. In this paper we propose an approach based on a pattern for tweet iron detection. We proposed to use a pattern-based approach to make a feeling study. We also research the significance and the price for the grouping of each proposed feature set.</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":"IMPLEMENTATION OF SARCASM DETECTION ON TWITTER DATA","attachmentId":66086766,"attachmentType":"pdf","work_url":"https://www.academia.edu/45602559/IMPLEMENTATION_OF_SARCASM_DETECTION_ON_TWITTER_DATA","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/45602559/IMPLEMENTATION_OF_SARCASM_DETECTION_ON_TWITTER_DATA"><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="2" data-entity-id="116205791" 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/116205791/Sarcasm_Detection_From_Twitter_Database_Using_Text_Mining_Algorithms">Sarcasm Detection From Twitter Database Using Text Mining Algorithms</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="289345019" href="https://independent.academia.edu/TamannaSiddiqui8">Tamanna Siddiqui</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Sarcasm is well-defined as a cutting, frequently sarcastic remark intended to fast ridicule or dislike. Irony detection is the assignment of fittingly labeling the text as’ Sarcasm’ or ’non- Sarcasm.’ There is a challenging task owing to the deficiency of facial expressions and intonation in the text. Social media and micro-blogging websites are extensively explored for getting the information to extract the opinion of the target because a huge of text data existence is put out into the open field into social media like Twitter. Such large, openly available text data could be utilized for a variety of researches. Here we applied text data set for classifying Sarcasm and experiments have been made from the textual data extracted from the Twitter data set. Text data set downloaded from Kaggle, including 1984 tweets that collected from Twitter. These data already have labels here. In this paper, we apply these data to train our model Classifiers for different algorithms to see the abil...</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":"Sarcasm Detection From Twitter Database Using Text Mining Algorithms","attachmentId":112400987,"attachmentType":"pdf","work_url":"https://www.academia.edu/116205791/Sarcasm_Detection_From_Twitter_Database_Using_Text_Mining_Algorithms","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/116205791/Sarcasm_Detection_From_Twitter_Database_Using_Text_Mining_Algorithms"><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="72281438" 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/72281438/Sarcasm_Detection_in_Tweets_A_Feature_based_Approach_using_Supervised_Machine_Learning_Models">Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models</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="24913480" href="https://nstu.academia.edu/ratnadipkuri">Ratnadip Kuri</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Sarcasm (i.e., the use of irony to mock or convey contempt) detection in tweets and other social media platforms is one of the problems facing the regulation and moderation of social media content. Sarcasm is difficult to detect, even for humans, due to the deliberate ambiguity in using words. Existing approaches to automatic sarcasm detection primarily rely on lexical and linguistic cues. However, these approaches have produced little or no significant improvement in terms of the accuracy of sentiment. We propose implementing a robust and efficient system to detect sarcasm to improve accuracy for sentiment analysis. In this study, four sets of features include various types of sarcasm commonly used in social media. These feature sets are used to classify tweets into sarcastic and nonsarcastic. This study reveals a sarcastic feature set with an effective supervised machine learning model, leading to better accuracy. Results show that Decision Tree (91.84%) and Random Forest (91.90%)...</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":"Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models","attachmentId":81270906,"attachmentType":"pdf","work_url":"https://www.academia.edu/72281438/Sarcasm_Detection_in_Tweets_A_Feature_based_Approach_using_Supervised_Machine_Learning_Models","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/72281438/Sarcasm_Detection_in_Tweets_A_Feature_based_Approach_using_Supervised_Machine_Learning_Models"><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="4" data-entity-id="48857587" 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/48857587/A_Review_on_Sarcasm_Detection_Based_on_Machine_Learning">A Review on Sarcasm Detection Based on Machine 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="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, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Sarcasm is a subtle form of irony, which can be widely used social networks such as twitter. It is usually used to transmit hidden information, a message sent by people. Due to a different purposes Sarcasm can be used like criticism and ridicule. But even this is difficult for a person to recognize. The sarcastic reorganization system is very helpful for the improvement of automatic sentiment analysis collected from different social networks and microblogging sites. Sentiment analysis refers to internet users of a particular community, expressed attitudes and opinions of identification and aggregation. To detecting sarcasm we propose a pattern-based approach using Twitter data. We proposes four sets of features that include a lot of specific sarcasm. We use them to classify tweets as sarcastic and non-sarcastic. We also study each of the proposed feature sets and evaluate its additional cost classifications.</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":"A Review on Sarcasm Detection Based on Machine Learning","attachmentId":67271578,"attachmentType":"pdf","work_url":"https://www.academia.edu/48857587/A_Review_on_Sarcasm_Detection_Based_on_Machine_Learning","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/48857587/A_Review_on_Sarcasm_Detection_Based_on_Machine_Learning"><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="97717304" 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/97717304/Automatic_identification_of_sarcasm_in_tweets_and_customer_reviews">Automatic identification of sarcasm in tweets and customer reviews</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="144962856" href="https://independent.academia.edu/DrWaqarMehmood">Dr. Waqar Mehmood</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Intelligent &amp; Fuzzy Systems, 2019</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 identification of sarcasm in tweets and customer reviews","attachmentId":99264073,"attachmentType":"pdf","work_url":"https://www.academia.edu/97717304/Automatic_identification_of_sarcasm_in_tweets_and_customer_reviews","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/97717304/Automatic_identification_of_sarcasm_in_tweets_and_customer_reviews"><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="6" data-entity-id="95407473" 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/95407473/Classification_of_Sarcastic_and_Non_Sarcastic_Tweets_Using_Machine_Learning">Classification of Sarcastic and Non-Sarcastic Tweets Using Machine 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="211789363" href="https://independent.academia.edu/StudiesCentralAsian">Central Asian Studies</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Central Asian Journal of Theoretical and Applied Science, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">When someone is being sarcastic, they are expressing their negative emotions through the use of positive or exaggerated positive language. A person's tone of voice and body language, such as eye rolling, hand gestures, etc., might give away their sarcasm. Without these non-verbal cues, such as tone of voice and body language, a human being would have a very difficult time detecting sarcasm in written data. These difficulties explain the growing interest in sarcasm detection of social media text, particularly tweets. Major difficulties arise from analysing the ever-increasing volume of tweets. We suggested a machine learning-based framework that can collect tweets in real time and analyse them with algorithms that can accurately detect sarcastic sentiment. We find that the analysis and processing time under an ML-based framework vastly surpasses the traditional methods and is better suited for continuously streaming tweets in real time.</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":"Classification of Sarcastic and Non-Sarcastic Tweets Using Machine Learning","attachmentId":97599043,"attachmentType":"pdf","work_url":"https://www.academia.edu/95407473/Classification_of_Sarcastic_and_Non_Sarcastic_Tweets_Using_Machine_Learning","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/95407473/Classification_of_Sarcastic_and_Non_Sarcastic_Tweets_Using_Machine_Learning"><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="44283414" 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/44283414/Sentiment_Analysis_Sarcasm_Detection_in_Twitter">Sentiment Analysis -Sarcasm Detection 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="2594470" href="https://independent.academia.edu/iosrjournals">IOSR Journals</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Sarcasm is the way of expressing opposite of what exactly is given. Generally, people use sarcasm to criticize. Sarcasm is merely a synonym of irony. Sarcasm is prevalent everywhere including social media. Twitter is a micro-blogging platform extensively used by people to express thoughts, reviews. It is a new trend to post sarcastic statements in order to avoid direct negativity. Hence it is necessary to implement an automated system that will be capable of understanding whether a statement is sarcastic or not. Already there are some systems that are implemented to meet this goal. The aim here is to develop a simpler model for finding the sarcasm in statements using NLP and an algorithm which produces results of good accuracy.</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":"Sentiment Analysis -Sarcasm Detection in Twitter","attachmentId":64663718,"attachmentType":"pdf","work_url":"https://www.academia.edu/44283414/Sentiment_Analysis_Sarcasm_Detection_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/44283414/Sentiment_Analysis_Sarcasm_Detection_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="8" data-entity-id="94257020" 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/94257020/Sarcasm_Detection_in_Tweets">Sarcasm Detection in 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="242204361" href="https://independent.academia.edu/AshwinBhat15">Ashwin Bhat</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017</p><p class="ds-related-work--abstract ds2-5-body-sm">Recognizing sarcasm in text is an important task for Natural Language processing to avoid misinterpretation of sarcastic statements as literal statements. Accuracy and robustness of NLP models are often affected by untruthful sentiments that are often of sarcastic nature. Thus, it is important to filter out noisy data from the training data inputs for various NLP related tasks. For example, a sentence like ”So thrilled to be on call for work the entire weekend!” could be naively classified as a sentence with a high positive sentiment. However, its actually the negative sentiment that is cleverly implied through sarcasm.</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":"Sarcasm Detection in Tweets","attachmentId":96764423,"attachmentType":"pdf","work_url":"https://www.academia.edu/94257020/Sarcasm_Detection_in_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/94257020/Sarcasm_Detection_in_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 class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="33604761" 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/33604761/Proposed_Approach_for_Sarcasm_Detection_in_Twitter">Proposed Approach for Sarcasm Detection 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="5738197" href="https://cub.academia.edu/shubhodipsaha">shubhodip saha</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Background: Sarcasm detection in twitter is a very important task as it had helped in the analysis of tweets. With the help of sarcasm detection, companies could analyze the feelings of user about their products. This is helpful for companies, as the companies could improve their quality of product. Methods: For preprocessing of data TextBlob is used. TextBlob is a package installed in Natural Language Toolkit. The preprocessing steps include tokenization, part of speech tagging and parsing. The stop words are removed using python programming. The stop words corpus which consist of 2400 stop words and which is distributed with NLTK have been used. RapidMiner is used for finding polarity and subjectivity of tweets. TextBlob is used for finding the polarity and subjectivity confidence. Weka is used for calculating the accuracy of tweets based on Naïve Bayes classifier and SVM classifiers. Findings: The paper provides the polarity of tweets which include whether the tweet is positive, negative or neutral. Polarity confidence and subjectivity confidence are also found. Accuracy of tweets are found using Naïve Bayes and SVM classifiers. Applications: Sarcasm Detection could be helpful in analyzing the exact opinion of the user about a certain thing.</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":"Proposed Approach for Sarcasm Detection in Twitter","attachmentId":53624134,"attachmentType":"pdf","work_url":"https://www.academia.edu/33604761/Proposed_Approach_for_Sarcasm_Detection_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/33604761/Proposed_Approach_for_Sarcasm_Detection_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></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":54946723,"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":54946723,"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_54946723" 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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