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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.16748 |
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| _version_ | 1866910311271890944 |
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| author | Saruar, S. S. Nusrat Sadia |
| author_facet | Saruar, S. S. Nusrat Sadia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework Saruar, S. S. Nusrat Sadia Computation and Language 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. |
| title | Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.16748 |