Turkish Text Classification Based On Wrapper Feature Selection Using Particle Swarm Optimization

Abstract

The vast majority of the digital era data is stored as text. Text mining is an integral part of data mining. Text classification (TC) is a natural language processing (NLP) operation often needed in text mining. This operation is needed in numerous kinds of research such as information retrieval, document classification, language detection, sentiment analysis, etc. According to the literature, the filter feature selection methods have often been applied to reduce the dimensionality of data in Turkish TC. However, the wrapper-based feature selection methods can provide better classification accuracies than the filter methods. Motivated by this idea, a Turkish TC method based on wrapper feature selection using particle swarm optimization algorithm (PSO) and multinomial naive bayes (MNB) classifier is proposed in this study. TTC-3600 Turkish news texts are used for TC in the experiments. The proposed method achieves a classification accuracy of 94.55% on TTC-3600 Turkish news text dataset by using stemming Tf-Idf features. Hence, it produces competitive accuracies to the cutting-edge Turkish TC methods.