@inproceedings{jayaraman-etal-2025-analysisarchitects-dravidianlangtech,
title = "{A}nalysis{A}rchitects@{D}ravidian{L}ang{T}ech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of {T}amil",
author = "Jayaraman, Abirami and
Shanmugam, Aruna Devi and
Sasikumar, Dharunika and
B, Bharathi",
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-1.105/",
doi = "10.18653/v1/2025.dravidianlangtech-1.105",
pages = "614--618",
ISBN = "979-8-89176-228-2",
abstract = "Sentiment analysis is recognized as an important area in Natural Language Processing (NLP) that aims at understanding and classifying opinions or emotions in text. In the political field, public sentiment is analyzed to gain insight into opinions, address issues, and shape better policies. Social media platforms like Twitter (now X) are widely used to express thoughts and have become a valuable source of real-time political discussions. In this paper, the shared task of Political Multiclass Sentiment Analysis of Tamil tweets is examined, where the objective is to classify tweets into specific sentiment categories. The proposed approach is explained, which involves preprocessing Tamil text, extracting useful features, and applying machine learning and deep learning models for classification. The effectiveness of the methods is demonstrated through experimental results and the challenges encountered while working on the analysis of Tamil political sentiment are discussed."
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%0 Conference Proceedings
%T AnalysisArchitects@DravidianLangTech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of Tamil
%A Jayaraman, Abirami
%A Shanmugam, Aruna Devi
%A Sasikumar, Dharunika
%A B, Bharathi
%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 jayaraman-etal-2025-analysisarchitects-dravidianlangtech
%X Sentiment analysis is recognized as an important area in Natural Language Processing (NLP) that aims at understanding and classifying opinions or emotions in text. In the political field, public sentiment is analyzed to gain insight into opinions, address issues, and shape better policies. Social media platforms like Twitter (now X) are widely used to express thoughts and have become a valuable source of real-time political discussions. In this paper, the shared task of Political Multiclass Sentiment Analysis of Tamil tweets is examined, where the objective is to classify tweets into specific sentiment categories. The proposed approach is explained, which involves preprocessing Tamil text, extracting useful features, and applying machine learning and deep learning models for classification. The effectiveness of the methods is demonstrated through experimental results and the challenges encountered while working on the analysis of Tamil political sentiment are discussed.
%R 10.18653/v1/2025.dravidianlangtech-1.105
%U https://aclanthology.org/2025.dravidianlangtech-1.105/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.105
%P 614-618
Markdown (Informal)
[AnalysisArchitects@DravidianLangTech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of Tamil](https://aclanthology.org/2025.dravidianlangtech-1.105/) (Jayaraman et al., DravidianLangTech 2025)
ACL
- Abirami Jayaraman, Aruna Devi Shanmugam, Dharunika Sasikumar, and Bharathi B. 2025. AnalysisArchitects@DravidianLangTech 2025: Machine Learning Approach to Political Multiclass Sentiment Analysis of Tamil. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 614–618, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.