TwitterSentiDetector: a domain-independent Twitter sentiment analyser

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dc.contributor.authorKabakuş, Abdullah Talha
dc.contributor.authorKara, Resul
dc.date.accessioned2020-04-30T23:46:50Z
dc.date.available2020-04-30T23:46:50Z
dc.date.issued2018
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionKabakus, Abdullah Talha/0000-0003-2181-4292; Kara, Resul/0000-0001-8902-6837en_US
dc.descriptionWOS: 000437366500001en_US
dc.description.abstractSentiment analysis has become more crucial after the rise of social media, especially the Twitter since it provides structured and publicly available data. TwitterSentiDetector is a domain-dependent and unsupervised Twitter sentiment analyser that focuses on the differences occurred by the informal language used in Twitter. TwitterSentiDetector uses natural language processing techniques alongside the proposed linguistic methods to classify sentiments of tweets into positive, negative, and neutral through the polarity scores obtained from sentiment lexicons. According to tests on widely used Twitter data-sets that contain manually detected sentiments labels alongside tweets, TwitterSentiDetector's sentiment detection ratio is calculated as up to 69%. When the target sentiment classes are decreased to positive and negative, the detection ratio is increased up to 87%. The results are calculated very similarly when the same data-set is evaluated by the proposed tweet-level context aware sentiment analysis module which confirms the validity of each approach.en_US
dc.identifier.doi10.1080/03155986.2017.1340797en_US
dc.identifier.endpage162en_US
dc.identifier.issn0315-5986
dc.identifier.issn1916-0615
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage137en_US
dc.identifier.urihttps://doi.org/10.1080/03155986.2017.1340797
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5287
dc.identifier.volume56en_US
dc.identifier.wosWOS:000437366500001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofInforen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTwitter sentiment analysisen_US
dc.subjectsentiment analysisen_US
dc.subjectnatural language processingen_US
dc.subjectsocial media miningen_US
dc.subjectsentiment detectionen_US
dc.titleTwitterSentiDetector: a domain-independent Twitter sentiment analyseren_US
dc.typeArticleen_US

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