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

arXiv: https://arxiv.org/abs/1701.08694 · HTML: https://mlsanity.com/paper/1701.08694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022)
- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)

## Abstract (arXiv)

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.