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The Importance of preprocessing in Turkish Text Classification

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dc.contributor.authorAcikalin, Buse
dc.contributor.authorBayazit, Nilgun Guler
dc.date.accessioned2026-06-27T13:58:33Z
dc.date.issued2016
dc.description.abstractIn this study, the effects of the application of stop words filtering and stemming methods on the classification of Turkish Texts. The documents in a corpus that consists of summaries of conference and journal articles classified by Naive Bayes, Support Vector Machines and Random Forests methods and their performers have been compaired. All the models that have employed preprocessing with stemming and stop words elimination have yielded between 2.26% and 4.94% improvement in performance to the models that have not employed such preprocessing.en
dc.identifier.endpage2056
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.startpage2053
dc.identifier.urihttps://hdl.handle.net/20.500.14981/56132
dc.identifier.wos000391250900490
dc.language.isotur
dc.publisherIEEE
dc.relation.conference24th Signal Processing and Communication Application Conference (SIU)
dc.relation.ispartof2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU)
dc.subjectText Mining
dc.subjectLatent Dirichlet Allocation
dc.subjectTopic Models
dc.subjectSupport Vector Machine
dc.subjectNaive Bayes
dc.subjectRandom Forest
dc.subjectEngineering
dc.titleThe Importance of preprocessing in Turkish Text Classification
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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