@inproceedings{eram-etal-2025-eureka,
title = "Eureka-{CIOL}@{D}ravidian{L}ang{T}ech 2025: Using Customized {BERT}s for Sentiment Analysis of {T}amil Political Comments",
author = "Eram, Enjamamul Haque and
Ahmed, Anisha and
Mitu, Sabrina Afroz and
Wasi, Azmine Toushik",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.2/",
doi = "10.18653/v1/2025.dravidianlangtech-1.2",
pages = "6--11",
ISBN = "979-8-89176-228-2",
abstract = "Sentiment analysis on social media platforms plays a crucial role in understanding public opinion and the decision-making process on political matters. As a significant number of individuals express their views on social media, analyzing these opinions is essential for monitoring political trends and assessing voter sentiment. However, sentiment analysis for low-resource languages, such as Tamil, presents considerable challenges due to the limited availability of annotated datasets and linguistic complexities. To address this gap, we utilize a novel dataset encompassing seven sentiment classes, offering a unique opportunity to explore sentiment variations in Tamil political discourse. In this study, we evaluate multiple pre-trained models from the Hugging Face library and experiment with various hyperparameter configurations to optimize model performance. Our findings aim to contribute to the development of more effective sentiment analysis tools tailored for low-resource languages, ultimately empowering Tamil-speaking communities by providing deeper insights into their political sentiments. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Eureka-Sentiment-Analysis-Tamil"
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%0 Conference Proceedings
%T Eureka-CIOL@DravidianLangTech 2025: Using Customized BERTs for Sentiment Analysis of Tamil Political Comments
%A Eram, Enjamamul Haque
%A Ahmed, Anisha
%A Mitu, Sabrina Afroz
%A Wasi, Azmine Toushik
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F eram-etal-2025-eureka
%X Sentiment analysis on social media platforms plays a crucial role in understanding public opinion and the decision-making process on political matters. As a significant number of individuals express their views on social media, analyzing these opinions is essential for monitoring political trends and assessing voter sentiment. However, sentiment analysis for low-resource languages, such as Tamil, presents considerable challenges due to the limited availability of annotated datasets and linguistic complexities. To address this gap, we utilize a novel dataset encompassing seven sentiment classes, offering a unique opportunity to explore sentiment variations in Tamil political discourse. In this study, we evaluate multiple pre-trained models from the Hugging Face library and experiment with various hyperparameter configurations to optimize model performance. Our findings aim to contribute to the development of more effective sentiment analysis tools tailored for low-resource languages, ultimately empowering Tamil-speaking communities by providing deeper insights into their political sentiments. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Eureka-Sentiment-Analysis-Tamil
%R 10.18653/v1/2025.dravidianlangtech-1.2
%U https://aclanthology.org/2025.dravidianlangtech-1.2/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.2
%P 6-11
Markdown (Informal)
[Eureka-CIOL@DravidianLangTech 2025: Using Customized BERTs for Sentiment Analysis of Tamil Political Comments](https://aclanthology.org/2025.dravidianlangtech-1.2/) (Eram et al., DravidianLangTech 2025)
ACL
- Enjamamul Haque Eram, Anisha Ahmed, Sabrina Afroz Mitu, and Azmine Toushik Wasi. 2025. Eureka-CIOL@DravidianLangTech 2025: Using Customized BERTs for Sentiment Analysis of Tamil Political Comments. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 6–11, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.