Abstract
Emotion recognition for high-resource languages has progressed significantly. However, resource-constrained languages such as Bengali have not advanced notably due to the lack of large benchmark datasets. Besides this, the need for more Bengali language processing tools makes the emotion recognition task more challenging and complicated. Therefore, we developed the largest dataset in this paper, consisting of almost 12k Bengali texts with six basic emotions. Then, we conducted experiments on our dataset to establish the baseline performance applying machine learning, deep learning, and transformer-based models as emotion classifiers. The experimental results demonstrate that the models achieved promising performance in Bengali emotion recognition.- Anthology ID:
- 2023.icon-1.55
- Volume:
- Proceedings of the 20th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2023
- Address:
- Goa University, Goa, India
- Editors:
- Jyoti D. Pawar, Sobha Lalitha Devi
- Venue:
- ICON
- SIG:
- SIGLEX
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 579–587
- Language:
- URL:
- https://aclanthology.org/2023.icon-1.55
- DOI:
- Cite (ACL):
- Atabuzzaman Md., Maksuda Bilkis Baby, and Shajalal Md.. 2023. Transformer-based Bengali Textual Emotion Recognition. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 579–587, Goa University, Goa, India. NLP Association of India (NLPAI).
- Cite (Informal):
- Transformer-based Bengali Textual Emotion Recognition (Md. et al., ICON 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.icon-1.55.pdf