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Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general" /> <meta name="twitter:image" content="https://0.academia-photos.com/31663136/9470372/10552222/s200_mihaela.cocea.png" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter" /> <meta property="og:title" content="Detecting sarcasm from students' feedback in Twitter" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general" /> <meta property="article:author" content="https://port.academia.edu/MihaelaCocea" /> <meta name="description" content="Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general" /> <title>(PDF) Detecting sarcasm from students' feedback in Twitter | Mihaela Cocea - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'd18f50785c7c3f3456076ad60aff04e5544ff0de'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733253540000); window.Aedu.timeDifference = new Date().getTime() - 1733253540000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students\u0026amp;#39; feedback collected via Twitter.","author":[{"@context":"https://schema.org","@type":"Person","name":"Mihaela Cocea"}],"contributor":[],"dateCreated":"2015-09-08","dateModified":"2019-09-14","headline":"Detecting sarcasm from students' feedback in Twitter","image":"https://attachments.academia-assets.com/38703091/thumbnails/1.jpg","inLanguage":"en","keywords":["Data Mining","Sentiment Analysis","Text Mining","Educational Data Mining","Sarcasm Detection","Students Feedback"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"port"}],"thumbnailUrl":"https://attachments.academia-assets.com/38703091/thumbnails/1.jpg","url":"https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter"}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-8d679e925718b5e8e4b18e9a4fab37f7eaa99e43386459376559080ac8f2856a.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-3cea6e0ad4715ed965c49bfb15dedfc632787b32ff6d8c3a474182b231146ab7.css" /><link rel="stylesheet" media="all" 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Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students' feedback collected via Twitter. "},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Detecting sarcasm from students' feedback in Twitter","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [31663136]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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":38703091,"attachmentType":"pdf"}"><img alt="First page of “Detecting sarcasm from students' feedback in Twitter”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/38703091/mini_magick20190224-2961-nuejjk.png?1551020138" /><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">Detecting sarcasm from students' feedback in Twitter</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="31663136" href="https://port.academia.edu/MihaelaCocea"><img alt="Profile image of Mihaela Cocea" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/31663136/9470372/10552222/s65_mihaela.cocea.png" />Mihaela Cocea</a></div><div class="ds-work-card--detail"></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students' feedback collected via Twitter. </p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":38703091,"attachmentType":"pdf","workUrl":"https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":38703091,"attachmentType":"pdf","workUrl":"https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="38703091" data-landing_url="https://www.academia.edu/15510378/Detecting_sarcasm_from_students_feedback_in_Twitter" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" 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="1" 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="2" 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. 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Complete information is being put out on the domains directly through social media. Sarcastic tweets can mislead processing activities and end in wrong classification. This paper compares various classification algorithms like Random Forest, SVC, Logistic Regression, Linear SVC and Gaussian Naïve Bayes to detect sarcasm within the input given by the user, the simplest classifier is chosen and paired with various pre-processing and filtering techniques using emojis to supply the simplest possible output. The emojis being the central idea introduced through this paper, the obtained results may be used as an input for other research and applications.</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 Analysis Using Machine Learning","attachmentId":94323603,"attachmentType":"pdf","work_url":"https://www.academia.edu/90884373/Sarcasm_Analysis_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/90884373/Sarcasm_Analysis_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="6" data-entity-id="66055588" 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/66055588/An_Approach_to_Detect_Sarcasm_in_Tweets">An Approach to Detect Sarcasm 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="82776508" href="https://independent.academia.edu/godarajyoti">jyoti godara</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">When academic and business ventures are discussed, electronic documents form the crucial part of receiving and transferring information. 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="7" 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="8" data-entity-id="80421312" 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/80421312/_404_Recognition_of_Sarcasm_in_Tweets_Based_on_Concept_Level_Sentiment_Analysis_and_Supervised_Learning_Approaches">!404 Recognition of Sarcasm in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches</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="111473367" href="https://independent.academia.edu/PTungthamthiti">Piyoros Tungthamthiti</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2015</p><p class="ds-related-work--abstract ds2-5-body-sm">Sarcasm is a form of communication that is intended to mock or harass someone by us-ing words with the opposite of their literal meaning. However, identification of sarcasm is somewhat difficult due to the gap between its literal and intended meaning. Recognition of sarcasm is a task that can potentially pro-vide a lot of benefits to other areas of nat-ural language processing. In this research, we propose a new method to identify sarcasm in tweets that focuses on several approaches: 1) sentiment analysis, 2) concept level and common-sense knowledge 3) coherence and 4) machine learning classification. We will use support vector machine (SVM) to classify sarcastic tweet based on our proposed features as well as ordinary N-grams. Our proposed classifier is an ensemble of two SVMs with two different feature sets. The results of the experiment show our method outperforms the baseline method and achieves 80 % accuracy. 1</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":"!404 Recognition of Sarcasm in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches","attachmentId":86808736,"attachmentType":"pdf","work_url":"https://www.academia.edu/80421312/_404_Recognition_of_Sarcasm_in_Tweets_Based_on_Concept_Level_Sentiment_Analysis_and_Supervised_Learning_Approaches","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/80421312/_404_Recognition_of_Sarcasm_in_Tweets_Based_on_Concept_Level_Sentiment_Analysis_and_Supervised_Learning_Approaches"><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="114354902" 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/114354902/Sarcasm_Detection_Beyond_using_Lexical_Features">Sarcasm Detection Beyond using Lexical Features</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="281299050" href="https://independent.academia.edu/JosephOluwaseyi4">Joseph Oluwaseyi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Database Management Systems, 2020</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 Beyond using Lexical Features","attachmentId":111075304,"attachmentType":"pdf","work_url":"https://www.academia.edu/114354902/Sarcasm_Detection_Beyond_using_Lexical_Features","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/114354902/Sarcasm_Detection_Beyond_using_Lexical_Features"><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":38703091,"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":38703091,"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_38703091" 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. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="80421137" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/80421137/Recognition_of_Sarcasm_in_Tweets_Based_on_Concept_Level_Sentiment_Analysis_and_Supervised_Learning_Approaches">Recognition of Sarcasm in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="111473367" href="https://independent.academia.edu/PTungthamthiti">Piyoros Tungthamthiti</a></div><p 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data-collection-position="7" data-entity-id="121127329" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/121127329/A_Novel_Integrated_Framework_for_Sarcasm_Detection_in_Social_Platform">A Novel Integrated Framework for Sarcasm Detection in Social Platform</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="38606100" href="https://caluniv.academia.edu/SamirBandyopadhyay">Samir Bandyopadhyay</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International journal of engineering and advanced technology, 2020</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 Novel Integrated Framework for Sarcasm Detection in Social 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