Bengali Social Media Comments Classification and Toxicity Detection Using Advanced Machine Learning Algorithms

DOI

10.2991/978-94-6239-664-7_38How to use a DOI?

Keywords

Bengali natural language processing; sentiment analysis; topic classification; toxicity detection; deep learning

Abstract

A major challenge in Bengali Natural Language Processing (NLP) is the availability of annotated data. This research introduces an authentic, publicly available dataset collected from social networks in compliance with platform terms of service. The dataset includes 10 topic and 4 sentiment classes. The authors propose a system for classifying topics and sentiments from Bengali comments using a CNN-BiLSTM-SVM ensemble stack, combining strengths of convolutional, recurrent, and statistical models. A key aspect of the research is identifying toxic and abusive comments. All steps taken to complete this experiment were carried out in accordance with widely accepted standards. Another difficulty was working with a low-resource language like Bengali, which required custom and extensive preprocessing and model design. All trained models were evaluated using standard metrics. In addition to the technical aspects, this project also focuses on improving the safety and moderation of the Internet for the Bengali language. A comment analysis tool for web view is also developed as part of this project.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rayhan Rafin
AU  - Mohammad Sohaib Islam Shibly
AU  - Tapasy Rabeya
PY  - 2026
DA  - 2026/06/08
TI  - Bengali Social Media Comments Classification and Toxicity Detection Using Advanced Machine Learning Algorithms
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 549
EP  - 564
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_38
DO  - 10.2991/978-94-6239-664-7_38
ID  - Rafin2026
ER  -