@inproceedings{babu-etal-2025-cuet,
title = "{CUET}{\_}{N}etwork{S}ociety@{D}ravidian{L}ang{T}ech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of {T}amil {X} ({T}witter) Comments",
author = "Babu, Tofayel Ahmmed and
Ratul, MD Musa Kalimullah and
Aftahee, Sabik and
Hossain, Jawad and
Hoque, Mohammed Moshiul",
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-1.93/",
doi = "10.18653/v1/2025.dravidianlangtech-1.93",
pages = "536--542",
ISBN = "979-8-89176-228-2",
abstract = "Social media has become an established medium of public communication and opinions on every aspect of life, but especially politics. This has resulted in a growing need for tools that can process the large amount of unstructured data that is produced on these platforms providing actionable insights in domains such as social trends and political opinion. Low-resource languages like Tamil present challenges due to limited tools and annotated data, highlighting the need for NLP focus on understudied languages. To address this, a shared task has been organized by DravidianLangTech@NAACL 2025 for political sentiment analysis for low-resource languages, with a specific focus on Tamil. In this task, we have explored several machine learning methods such as SVM, AdaBoost, GB, deep learning methods including CNN, LSTM, GRU BiLSTM, and the ensemble of different deep learning models, and transformer-based methods including mBERT, T5, XLM-R. The mBERT model performed best by achieving a macro F1 score of 0.2178 and placing our team 22nd in the rank list."
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%0 Conference Proceedings
%T CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of Tamil X (Twitter) Comments
%A Babu, Tofayel Ahmmed
%A Ratul, MD Musa Kalimullah
%A Aftahee, Sabik
%A Hossain, Jawad
%A Hoque, Mohammed Moshiul
%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 babu-etal-2025-cuet
%X Social media has become an established medium of public communication and opinions on every aspect of life, but especially politics. This has resulted in a growing need for tools that can process the large amount of unstructured data that is produced on these platforms providing actionable insights in domains such as social trends and political opinion. Low-resource languages like Tamil present challenges due to limited tools and annotated data, highlighting the need for NLP focus on understudied languages. To address this, a shared task has been organized by DravidianLangTech@NAACL 2025 for political sentiment analysis for low-resource languages, with a specific focus on Tamil. In this task, we have explored several machine learning methods such as SVM, AdaBoost, GB, deep learning methods including CNN, LSTM, GRU BiLSTM, and the ensemble of different deep learning models, and transformer-based methods including mBERT, T5, XLM-R. The mBERT model performed best by achieving a macro F1 score of 0.2178 and placing our team 22nd in the rank list.
%R 10.18653/v1/2025.dravidianlangtech-1.93
%U https://aclanthology.org/2025.dravidianlangtech-1.93/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.93
%P 536-542
Markdown (Informal)
[CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of Tamil X (Twitter) Comments](https://aclanthology.org/2025.dravidianlangtech-1.93/) (Babu et al., DravidianLangTech 2025)
ACL
- Tofayel Ahmmed Babu, MD Musa Kalimullah Ratul, Sabik Aftahee, Jawad Hossain, and Mohammed Moshiul Hoque. 2025. CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of Tamil X (Twitter) Comments. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 536–542, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.