# Supervised learning Methods for Bangla Web Document Categorization

arXiv: https://arxiv.org/abs/1410.2045 · HTML: https://mlsanity.com/paper/1410.2045/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2014.

## Built on by (most cited first, 5 of 5)

- [A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models](https://mlsanity.com/paper/2107.03844/) (2021)
- [Crosslingual Retrieval Augmented In-context Learning for Bangla](https://mlsanity.com/paper/2311.00587/) (2023)
- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset](https://mlsanity.com/paper/2308.13545/) (2023)
- [BanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning](https://mlsanity.com/paper/2511.23264/) (2025)

## Abstract (arXiv)

This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5), K-Nearest Neighbour (KNN), Na"ive Bays (NB), and Support Vector Machine (SVM) for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.