Author Detection by Using Different Term Weighting Schemes

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dc.authorid0000-0003-4351-2244
dc.authorid0000-0003-4842-2635
dc.authorscopusid11539603200
dc.authorscopusid54783608800
dc.authorwosidUzun, Erdinç/AAG-5529-2019
dc.contributor.authorTüfekci, Pınar
dc.contributor.authorUzun, Erdinç
dc.date.accessioned2022-05-11T14:15:47Z
dc.date.available2022-05-11T14:15:47Z
dc.date.issued2013
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
dc.description.abstractIn this study, the impact of term weighting on author detection as a type of text classification is investigated. The feature vector being used to represent texts, consists of stem words as features and their weight values, which are obtained by applying 14 different term weighting schemes. The performances of these feature vectors for 3 different datasets in the author detection are tested with some classification methods such as Naive Bayes Multinominal (NBM), and Support Vector Machine (SVM), Decision Tree (C4.5), and Random Forrest (RF), and are compared with each other. As a result of that, the most successful classifier, which can predict the author of an article, is found as SVM classifier with 98.75% mean accuracy; the most successful term weighting scheme is found as ACTF.IDF.(ICF+1) with 91.54% general mean accuracy.
dc.identifier.isbn978-1-4673-5563-6
dc.identifier.isbn978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-84880911212
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6072
dc.identifier.wosWOS:000325005300031
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTufekçi, Pınar
dc.institutionauthorUzun, Erdinç
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectauthor detection
dc.subjectterm weighting schemes
dc.subjecttext classification
dc.subjectText Categorization
dc.titleAuthor Detection by Using Different Term Weighting Schemes
dc.title.alternativeFarkli terim agirliklandirma yöntemleri kullanarak yazar tanima]
dc.typeConference Object

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