@inproceedings{shanmugavadivel-etal-2025-team,
title = "{T}eam{\_}{C}atalysts@{D}ravidian{L}ang{T}ech 2025: Leveraging Political Sentiment Analysis using Machine Learning Techniques for Classifying {T}amil Tweets",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
K, Subhadevi and
Sivakumar, Sowbharanika Janani and
K, Rahul",
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-1.26/",
doi = "10.18653/v1/2025.dravidianlangtech-1.26",
pages = "157--161",
ISBN = "979-8-89176-228-2",
abstract = "This work proposed a methodology for assessing political sentiments in Tamil tweets using machine learning models. The approach addressed linguistic challenges in Tamil text, including cleaning, normalization, tokenization, and class imbalance, through a robust preprocessing pipeline. Various models, including Random Forest, Logistic Regression, and CatBoost, were applied, with Random Forest achieving a macro F1-score of 0.2933 and securing 8th rank among 153 participants in the Codalab competition. This accomplishment highlights the effectiveness of machine learning models in handling the complexities of multilingual, code-mixed, and unstructured data in Tamil political discourse. The study also emphasized the importance of tailored preprocessing techniques to improve model accuracy and performance. It demonstrated the potential of computational linguistics and machine learning in understanding political discourse in low-resource languages like Tamil, contributing to advancements in regional sentiment analysis."
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%0 Conference Proceedings
%T Team_Catalysts@DravidianLangTech 2025: Leveraging Political Sentiment Analysis using Machine Learning Techniques for Classifying Tamil Tweets
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A K, Subhadevi
%A Sivakumar, Sowbharanika Janani
%A K, Rahul
%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 shanmugavadivel-etal-2025-team
%X This work proposed a methodology for assessing political sentiments in Tamil tweets using machine learning models. The approach addressed linguistic challenges in Tamil text, including cleaning, normalization, tokenization, and class imbalance, through a robust preprocessing pipeline. Various models, including Random Forest, Logistic Regression, and CatBoost, were applied, with Random Forest achieving a macro F1-score of 0.2933 and securing 8th rank among 153 participants in the Codalab competition. This accomplishment highlights the effectiveness of machine learning models in handling the complexities of multilingual, code-mixed, and unstructured data in Tamil political discourse. The study also emphasized the importance of tailored preprocessing techniques to improve model accuracy and performance. It demonstrated the potential of computational linguistics and machine learning in understanding political discourse in low-resource languages like Tamil, contributing to advancements in regional sentiment analysis.
%R 10.18653/v1/2025.dravidianlangtech-1.26
%U https://aclanthology.org/2025.dravidianlangtech-1.26/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.26
%P 157-161
Markdown (Informal)
[Team_Catalysts@DravidianLangTech 2025: Leveraging Political Sentiment Analysis using Machine Learning Techniques for Classifying Tamil Tweets](https://aclanthology.org/2025.dravidianlangtech-1.26/) (Shanmugavadivel et al., DravidianLangTech 2025)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Subhadevi K, Sowbharanika Janani Sivakumar, and Rahul K. 2025. Team_Catalysts@DravidianLangTech 2025: Leveraging Political Sentiment Analysis using Machine Learning Techniques for Classifying Tamil Tweets. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 157–161, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.