Nitelik Çıkarımı Yöntemlerinin Türkçe Metinlerin Sınıflandırılmasına Etkisi

Abstract

Feature extraction is the most important preprocessing step of text classification task. Effects of preprocessing techniques on text mining for English have been extensively studied. However, studies for Turkish are limited and generally belong to a specific problem domain. In this study, we investigate the effects of feature extraction techniques on four different Turkish text classification problems including news classification, spam e-mail detection, sentiment analysis, and author detection to show the differences and similarities among the problems. We also propose a new feature selection method to reduce feature space. The experimental analysis has showed that, stopword removal improves classification performance. However, stemming does not make any positive effect on classification accuracy. The most successful term weighting methods are tf and tf*idf. The proposed feature selection method improves classification performance and has higher accuracy than the well-known methods.