Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach
Mesay Gemeda Yigezu, Selam Kanta, Olga Kolesnikova, Grigori Sidorov, Alexander Gelbukh
Abstract
This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.- Anthology ID:
- 2023.dravidianlangtech-1.36
- 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:
- 244–249
- Language:
- URL:
- https://aclanthology.org/2023.dravidianlangtech-1.36
- DOI:
- Cite (ACL):
- Mesay Gemeda Yigezu, Selam Kanta, Olga Kolesnikova, Grigori Sidorov, and Alexander Gelbukh. 2023. Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 244–249, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach (Yigezu et al., DravidianLangTech-WS 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.dravidianlangtech-1.36.pdf