Turkish sentiment analysis: a comprehensive review

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dc.authorid0000-0001-5544-0925
dc.authorid0000-0002-9502-7817
dc.authorid0009-0009-9956-5434
dc.contributor.authorAltınel Girgin, Ayşe Bernaen_US
dc.contributor.authorGümüşçekiçci, Gizemen_US
dc.contributor.authorBirdemir, Nuri Canen_US
dc.date.accessioned2025-08-19T11:52:22Z
dc.date.available2025-08-19T11:52:22Z
dc.date.issued2024-08
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractSentiment analysis (SA) is a very popular research topic in the text mining field. SA is the process of textual mining in which the meaning of a text is detected and extracted. One of the key aspects of SA is to analyze the body of a text to determine its polarity to understand the opinions it expresses. Substantial amounts of data are produced by online resources such as social media sites, blogs, news sites, etc. Due to this reason, it is impossible to process all of this data without automated systems, which has contributed to the rise in popularity of SA in recent years. SA is considered to be extremely essential, mostly due to its ability to analyze mass opinions. SA, and Natural Language Processing (NLP) in particular, has become an overwhelmingly popular topic as social media usage has increased. The data collected from social media has sourced numerous different SA studies due to being versatile and accessible to the masses. This survey presents a comprehensive study categorizing past and present studies by their employed methodologies and levels of sentiment. In this survey, Turkish SA studies were categorized under three sections. These are Dictionary-based, Machine Learning-based, and Hybrid-based. Researchers can discover, compare, and analyze properties of different Turkish SA studies reviewed in this survey, as well as obtain information on the public dataset and the dictionaries used in the studies. The main purpose of this study is to combine Turkish SA approaches and methods while briefly explaining its concepts. This survey uniquely categorizes a large number of related articles and visualizes their properties. To the best of our knowledge, there is no such comprehensive and up-to-date survey that strictly covers Turkish SA which mainly concerns analysis of sentiment levels. Furthermore, this survey contributes to the literature due to its unique property of being the first of its kind.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationAltınel Girgin, A. B., Gümüşçekiçci, G. & Birdemir, N. C. (2024). Turkish sentiment analysis: a comprehensive review. Sigma Journal of Engineering and Natural Sciences, 42(4), 1292-1314. doi:10.14744/sigma.2024.00033en_US
dc.identifier.doi10.14744/sigma.2024.00033
dc.identifier.endpage1314
dc.identifier.issn1304-7205
dc.identifier.issn1304-7191
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85200762519
dc.identifier.scopusqualityQ4
dc.identifier.startpage1292
dc.identifier.urihttps://hdl.handle.net/11729/6627
dc.identifier.urihttps://doi.org/10.14744/sigma.2024.00033
dc.identifier.volume42
dc.identifier.wosWOS:001288574200014
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakEmerging Sources Citation Index (ESCI)en_US
dc.institutionauthorGümüşçekiçci, Gizemen_US
dc.institutionauthorid0000-0002-9502-7817
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherYildiz Technical Universityen_US
dc.relation.ispartofSigma Journal of Engineering and Natural Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.subjectNatural language processingen_US
dc.subjectPolarity detectionen_US
dc.subjectSentiment analysisen_US
dc.subjectSentiment classificationen_US
dc.titleTurkish sentiment analysis: a comprehensive reviewen_US
dc.typeReview Articleen_US
dspace.entity.typePublicationen_US

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