Reducing Feature Space and Analyzing Effects of Using Non Linear Kernels in SVM for Bangla News Categorization

Text categorization is a trending topic nowadays. In this paper, we analyzed some existing approaches for Bangla document categorization and proposed some modifications of them. Using our modified approach we achieved an accuracy of 92.79% which is the best accuracy so far in the dataset that we used which consists of more than 30000 documents. In short we used tf-idf mixed with term frequency threshold as our feature selection technique and SVM as our classifier to classify the documents. We also greatly reduced the feature space and computation time using our approach.