# Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning

arXiv: https://arxiv.org/abs/2304.04087 · HTML: https://mlsanity.com/paper/2304.04087/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 4 of 4)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://mlsanity.com/paper/2004.07807/) (2020)
- [Bangla hate speech detection on social media using attention-based recurrent neural network](https://mlsanity.com/paper/2203.16775/) (2022)
- [Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language](https://mlsanity.com/paper/2106.04506/) (2021)

## Built on by (most cited first, 6 of 6)

- [BIDWESH: A Bangla Regional Based Hate Speech Detection Dataset](https://mlsanity.com/paper/2507.16183/) (2025)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Assessing the Level of Toxicity Against Distinct Groups in Bangla Social Media Comments: A Comprehensive Investigation](https://mlsanity.com/paper/2409.17130/) (2024)
- [Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework](https://mlsanity.com/paper/2401.16748/) (2024)
- [BengaliSent140: A Large-Scale Bengali Binary Sentiment Dataset for Hate and Non-Hate Speech Classification](https://mlsanity.com/paper/2601.20129/) (2026)
- [Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection](https://mlsanity.com/paper/2607.11597/) (2026)

## Abstract (arXiv)

This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification