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Turkish Document Classification Based on Word2Vec and SVM Classifier

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dc.contributor.authorSahin, Gurkan
dc.date.accessioned2026-06-27T13:57:44Z
dc.date.issued2017
dc.description.abstractIn this study, Turkish texts belonging to different categories were classified by using word2vec word vectors. Firstly, vectors of the words in all the texts were extracted then, each text was represented in terms of the mean vectors of the words it contains. Texts were classified by SVM and 0.92 F measurement score was obtained for seven different categories. As a result, it was experimentally shown that word2vec is more successful than tf-idf based classification for Turkish document classification.en
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.14981/55955
dc.identifier.wos000413813100415
dc.language.isotur
dc.publisherIEEE
dc.relation.conference25th Signal Processing and Communications Applications Conference (SIU)
dc.relation.ispartof2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
dc.subjectdocument categorization
dc.subjectSVM
dc.subjectword2vec
dc.subjectAcoustics
dc.subjectComputer Science
dc.subjectEngineering
dc.subjectTelecommunications
dc.titleTurkish Document Classification Based on Word2Vec and SVM Classifier
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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