Sentiment Analysis Using Learning Approaches over Emojis for Turkish Tweets

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dc.authoridyildirim, savas/0000-0002-7764-2891|Yildiz, Tugba/0000-0002-8552-2806
dc.authorwosidyildirim, savas/AAG-4639-2019
dc.authorwosidYildiz, Tugba/ABC-5958-2020
dc.contributor.authorVelioglu, Riza
dc.contributor.authorYildiz, Tugba
dc.contributor.authorYildirim, Savas
dc.date.accessioned2024-07-18T20:51:08Z
dc.date.available2024-07-18T20:51:08Z
dc.date.issued2018
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGen_US
dc.description.abstractWith the rise of the usage and interest on social media platforms, emojis have become an increasingly important part of the written language and one of the most important signals for micro-blog sentiment analysis. In this paper, we employed and evaluated classification models using two different representations based on hag-of-words and fasilText to address the problem of sentiment analysis over emojis/emoticons for Turkish positive, negative and neutral tweets. At first, the hag of-words approach is used as a simple and efficient baseline method for tweet representation, where the classifiers such as Naive Bayes, Logistic Regression, Support Vector Machines, Decision Trees have been applied to these tweets. Secondly, we utilized fastText to represent tweets as word n-grams for sentiment analysis problem. The results show that there is no significant difference between the two models. While fastText shows 79% and the Linear Regression classifier obtains 77% Fl-score for binary classification, fastText performs 62% and Linear Regression has 58% Fl-score for multi-class classification. This study is considered as the first study that contributes to the literature by applying different vector representations such as bag-of-words and fastText to predict Turkish tweets over emojis. This study can also be utilized to predict emojis on social media context in the future.en_US
dc.description.sponsorshipBMBB,Istanbul Teknik Univ,Gazi Univ,ATILIM Univ,Int Univ Sarajevo,Kocaeli Univ,TURKiYE BiLiSiM VAKFIen_US
dc.identifier.endpage307en_US
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.scopus2-s2.0-85060616109en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage303en_US
dc.identifier.urihttps://hdl.handle.net/11411/8420
dc.identifier.wosWOS:000459847400057en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 3rd International Conference on Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSentiment Analysisen_US
dc.subjectEmoji Predictionen_US
dc.subjectBag Of Worden_US
dc.subjectFasttexten_US
dc.subjectDeep Learningen_US
dc.titleSentiment Analysis Using Learning Approaches over Emojis for Turkish Tweetsen_US
dc.typeConference Objecten_US

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