Author Detection by Using Different Term Weighting Schemes
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Dosyalar
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
Anahtar Kelimeler
author detection, term weighting schemes, text classification, Text Categorization
Kaynak
2013 21st Signal Processing and Communications Applications Conference (Siu)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
