Sentiment Analysis with Term Weighting and Word Vectors

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dc.authorid0000-0002-6123-5343en_US
dc.contributor.authorBilgin, Metin
dc.contributor.authorKöktaş, Haldun
dc.date.accessioned2021-03-20T20:12:30Z
dc.date.available2021-03-20T20:12:30Z
dc.date.issued2019
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractIt is the sentiment analysis with which it is fried to predict the sentiment being told in the texts in an area where Natural Language Processing (NLP) studies are being frequently used in recent years. In this study sentiment extraction has been made from Turkish texts and performances of methods that are used in text representation have been compared. In the study being conducted, besides Bag of Words (BoW) method which is traditionally used for the representation of texts, Word2Vec, which is word vector algorithm being developed in recent years and Doc2Vec, being document vector algorithm, have been used. For the study 5 different Machine Learning (ML) algorithms have been used to classify the texts being represented in 5 different ways on 3000 pieces of labeled tweets belonging to a telecom company. As a conclusion it was seen that Word2Vec, being among text representation methods and Random Forest, being among ML algorithms were most successful and most applicable ones. It is important as it is the first study with which BoW and word vectors have been compared for sentiment analysis in Turkish texts.en_US
dc.identifier.endpage959en_US
dc.identifier.issn1683-3198
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage953en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/593
dc.identifier.volume16en_US
dc.identifier.wosWOS:000483391200020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKöktaş, Haldun
dc.language.isoenen_US
dc.publisherZarka Private Univen_US
dc.relation.ispartofInternational Arab Journal Of Information Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWord2vecen_US
dc.subjectDoc2vecen_US
dc.subjectsentiment analysisen_US
dc.subjectmachine learningen_US
dc.subjectnatural language processingen_US
dc.titleSentiment Analysis with Term Weighting and Word Vectorsen_US
dc.typeArticleen_US

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Mekatronik Mühendisliği Bölümü Yayın Koleksiyonu
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