A Review on Supervised Machine Learning Text Categorization Approaches

Abstract

The inception of abundant and hefty digitized text documents has emerged in recent years. Such texts need to be categorized technically for storage and retrieval purpose. Text Categorization Machine Learning techniques exist to manage the substantial data. These techniques can be divided into Supervised and Unsupervised Machine Learning techniques. In this paper we review Supervised Machine learning techniques that deal with generating patterns and hypothesis according to the prior knowledge. The objective of this paper is to highlight important features and methods of Supervised Machine Learning techniques. Selecting important features and then classifying the text is necessary for effective text categorization. The performance of Text Categorization and dimensionality improves by performing feature selection in the existing techniques. The importance of feature selection is considered in this paper. The general comparison table of techniques is presented for in depth parametric assessment.