Implementing News Article Category Browsing Based on Text Categorization Technique

Abstract

We propose a feature called category browsing to enhance the full-text search function of Thai-language news article search engine. The category browsing allows users to browse and filter search results based on some predefined categories. To implement the category browsing feature, we applied and compared among several text categorization algorithms including decision tree, Naive Bayes (NB) and Support Vector Machines (SVM). To further increase the performance of text categorization, we performed evaluation among many feature selection techniques including document frequency thresholding (DF), information gain (IG) and x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (CHI). Based on our experiments using a large news corpus, the SVM algorithm with the IG feature selection yielded the best performance with the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> measure equal to 95.42%.