Sentiment classification performance analysis based on glove word embedding

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dc.authoridYasin Kırelli / 0000-0002-3605-8621en_US
dc.authoridŞebnem Özdemir / 0000-0001-6668-6285en_US
dc.authorscopusidYasin Kırelli / 57219179532
dc.authorscopusidŞebnem Özdemir / 57205204578
dc.authorwosidYasin Kırelli / HHC-1961-2022
dc.authorwosidŞebnem Özdemir / AAP-7345-2020
dc.contributor.authorKırelli, Yasin
dc.contributor.authorÖzdemir, Şebnem
dc.date.accessioned2021-07-05T13:28:15Z
dc.date.available2021-07-05T13:28:15Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractRepresentation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pre-trained, embedded and deterministic word embedding classification performance has analyzed by using Long Short-Term Memory (LSTM). The proposed LSTM based deep learning model has been tested on three different data sets and the results have been evaluateden_US
dc.identifier.citationKırelli, Y , Özdemir, Ş . (2021). Sentiment Classification Performance Analysis Based on Glove Word Embedding . Sakarya University Journal of Science , 25 (3) , 639-646 . DOI: 10.16984/saufenbilder.886583en_US
dc.identifier.doi10.16984/saufenbilder.886583en_US
dc.identifier.endpage646en_US
dc.identifier.issn2147-835Xen_US
dc.identifier.issue3en_US
dc.identifier.startpage639en_US
dc.identifier.trdizinid469602en_US
dc.identifier.urihttps://doi.org/10.16984/saufenbilder.886583
dc.identifier.urihttps://hdl.handle.net/20.500.12713/1880
dc.identifier.volume25en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorKırelli, Yasin
dc.institutionauthorÖzdemir, Şebnem
dc.language.isoenen_US
dc.publisherDergiParken_US
dc.relation.ispartofSakarya University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSentiment Classificationen_US
dc.subjectWord Embeddingen_US
dc.subjectWord Weighten_US
dc.subjectGlove Word Embeddingen_US
dc.titleSentiment classification performance analysis based on glove word embeddingen_US
dc.typeArticleen_US

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