Sentiment Analysis of Turkish Twitter Data

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dc.authoridShehu, Harisu Abdullahi/0000-0002-9689-3290
dc.authoridTOKAT, Sezai/0000-0003-0193-8220
dc.contributor.authorShehu, Harisu Abdullahi
dc.contributor.authorTokat, Sezai
dc.contributor.authorSharif, Md. Haidar
dc.contributor.authorUyaver, Sahin
dc.date.accessioned2025-02-20T08:42:17Z
dc.date.available2025-02-20T08:42:17Z
dc.date.issued2019
dc.departmentTürk-Alman Üniversitesien_US
dc.description3rd International Conference of Mathematical Sciences (ICMS) -- SEP 04-08, 2019 -- Maltepe Univ, Istanbul, TURKEYen_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.
dc.identifier.doi10.1063/1.5136197
dc.identifier.isbn978-0-7354-1930-8
dc.identifier.issn0094-243X
dc.identifier.scopus2-s2.0-85076778222
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.1063/1.5136197
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1625
dc.identifier.volume2183en_US
dc.identifier.wosWOS:000505225800092
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAmer Inst Physics
dc.relation.ispartofThird International Conference of Mathematical Sciences (Icms 2019)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250220
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 Data
dc.typeConference Object

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