A Comparative Study on Different Types of Approaches to Bengali document Categorization

TL;DR

This paper compares the performance of SVM, Naive Bayes, and SGD classifiers for Bengali document categorization, analyzing the impact of feature selection techniques like Chi-square and TFIDF on their effectiveness.

Contribution

It provides a comparative analysis of three supervised learning algorithms with different feature selection methods for Bengali document classification.

Findings

SVM outperforms other classifiers in accuracy.

Feature selection techniques significantly influence classification performance.

TFIDF with word analyzer improves results across classifiers.

Abstract

Document categorization is a technique where the category of a document is determined. In this paper three well-known supervised learning techniques which are Support Vector Machine(SVM), Na\"ive Bayes(NB) and Stochastic Gradient Descent(SGD) compared for Bengali document categorization. Besides classifier, classification also depends on how feature is selected from dataset. For analyzing those classifier performances on predicting a document against twelve categories several feature selection techniques are also applied in this article namely Chi square distribution, normalized TFIDF (term frequency-inverse document frequency) with word analyzer. So, we attempt to explore the efficiency of those three-classification algorithms by using two different feature selection techniques in this article.