Author Detection by Using Different Term Weighting Schemes

Abstract

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.