Abstract
Detecting abusive language in multimodal videos has become a pressing need in ensuring a safe and inclusive online environment. This paper focuses on addressing this challenge through the development of a novel approach for multimodal abusive language detection in Tamil videos and sentiment analysis for Tamil/Malayalam videos. By leveraging state-of-the-art models such as Multiscale Vision Transformers (MViT) for video analysis, OpenL3 for audio analysis, and the bert-base-multilingual-cased model for textual analysis, our proposed framework integrates visual, auditory, and textual features. Through extensive experiments and evaluations, we demonstrate the effectiveness of our model in accurately detecting abusive content and predicting sentiment categories. The limited availability of effective tools for performing these tasks in Dravidian Languages has prompted a new avenue of research in these domains.- Anthology ID:
- 2023.dravidianlangtech-1.18
- Volume:
- Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Sajeetha Thavareesan, Elizabeth Sherly
- Venues:
- DravidianLangTech | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 140–146
- Language:
- URL:
- https://aclanthology.org/2023.dravidianlangtech-1.18
- DOI:
- Cite (ACL):
- Abhinaba Bala and Parameswari Krishnamurthy. 2023. AbhiPaw@DravidianLangTech: Multimodal Abusive Language Detection and Sentiment Analysis. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 140–146, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- AbhiPaw@DravidianLangTech: Multimodal Abusive Language Detection and Sentiment Analysis (Bala & Krishnamurthy, DravidianLangTech-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.dravidianlangtech-1.18.pdf