Sentiment analysis of turkish twitter data

dc.contributor.authorShehu, Harisu Abdullahi
dc.contributor.authorTokat, Sezai
dc.contributor.authorSharif, Md. Haidar
dc.contributor.authorUyaver, Şahin
dc.date.accessioned2024-07-12T20:49:03Z
dc.date.available2024-07-12T20:49:03Z
dc.date.issued2019en_US
dc.departmentFakülteler, İnsan ve Toplum Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractIn this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data.en_US
dc.identifier.citationShehu H. A., Tokat, S., Sharif, M. H. ve Uyaver, Ş. (2019). Sentiment analysis of turkish twitter data. International Conference of Mathematical Sciences (ICMS 2019). s. 148.en_US
dc.identifier.endpage149en_US
dc.identifier.isbn978-605-2124-29-1
dc.identifier.startpage148en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/2097
dc.language.isoenen_US
dc.publisherMaltepe Üniversitesien_US
dc.relation.ispartofInternational Conference of Mathematical Sciences (ICMS 2019)en_US
dc.relation.publicationcategoryUluslararası Konferans Öğesi - Başka Kurum Yazarıen_US
dc.rightsCC0 1.0 Universal*
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.snmzKY01459
dc.subjectArtificial intelligenceen_US
dc.subjectClassifieren_US
dc.subjectMachine learningen_US
dc.subjectSentiment analysisen_US
dc.subjectTurkishen_US
dc.subjectTwitteren_US
dc.titleSentiment analysis of turkish twitter dataen_US
dc.typeArticle
dspace.entity.typePublication

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