Abstract
Trolling refers to any user behaviour on the internet to intentionally provoke or instigate conflict predominantly in social media. This paper aims to classify troll meme captions in Tamil-English code-mixed form. Embeddings are obtained for raw code-mixed text and the translated and transliterated version of the text and their relative performances are compared. Furthermore, this paper compares the performances of 11 different classification algorithms using Accuracy and F1- Score. We conclude that we were able to achieve a weighted F1 score of 0.74 through MuRIL pretrained model.- Anthology ID:
- 2022.dravidianlangtech-1.24
- Volume:
- Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 151–157
- Language:
- URL:
- https://aclanthology.org/2022.dravidianlangtech-1.24
- DOI:
- 10.18653/v1/2022.dravidianlangtech-1.24
- Cite (ACL):
- Achyuta V, Mithun Kumar S R, Aruna Malapati, and Lov Kumar. 2022. BPHC@DravidianLangTech-ACL2022-A comparative analysis of classical and pre-trained models for troll meme classification in Tamil. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 151–157, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- BPHC@DravidianLangTech-ACL2022-A comparative analysis of classical and pre-trained models for troll meme classification in Tamil (V et al., DravidianLangTech 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.dravidianlangtech-1.24.pdf