Exploring Feature Selection and Support Vector Machine in Text Categorization

Abstract

With the growing number of text documents in the Internet, it is difficult for users to search, find, manage and organize information quickly. Normally, text documents are classified manually and it is time-consuming. Text categorization is a process of assigning text documents into a set of fixed predefined categories. The high dimensionality of text documents made it difficult to categorize because text documents contain noise and useless data. This paper explored several methods of feature selection that can be used to reduce high dimensionality of feature space in text documents such as Information Gain, Gain Ratio, CHI-Squares, Mutual Information and Document frequency. Next, the study adopted text categorization using Support Vector Machines. The results showed that Support Vector Machines perform well and very fast both in training and testing datasets.