Algorithms for Text Categorization : A Comparative Study

Text Categorization’s significance is on a continuous acceleration due to the present mammoth escalation in textual data thrusting the importance of analysing and examining the methods for handling textual data. This paper discusses and compares six algorithms for Text categorization, such as: Naïve Bayes, Support Vector Machine, NGrams, K-Nearest Neighbourhood, Back Propagation Network and Genetic Algorithm. Furthermore, the performances of all these algorithms are compared and tabulated.