# Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework

arXiv: https://arxiv.org/abs/2401.16748 · HTML: https://mlsanity.com/paper/2401.16748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Multimodal Hate Speech Detection from Bengali Memes and Texts](https://mlsanity.com/paper/2204.10196/) (2022)
- [Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning](https://mlsanity.com/paper/2304.04087/) (2023)
- [Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks](https://mlsanity.com/paper/2304.02739/) (2023)

## Abstract (arXiv)

Racism is an alarming phenomenon in our country as well as all over the world. Every day we have come across some racist comments in our daily life and virtual life. Though we can eradicate this racism from virtual life (such as Social Media). In this paper, we have tried to detect those racist comments with NLP and deep learning techniques. We have built a novel dataset in the Bengali Language. Further, we annotated the dataset and conducted data label validation. After extensive utilization of deep learning methodologies, we have successfully achieved text detection with an impressive accuracy rate of 87.94% using the Ensemble approach. We have applied RNN and LSTM models using BERT Embeddings. However, the MCNN-LSTM model performed highest among all those models. Lastly, the Ensemble approach has been followed to combine all the model results to increase overall performance.