@inproceedings{s-etal-2025-ksk,
title = "{KSK}@{D}ravidian{L}ang{T}ech 2025: Political Multiclass Sentiment Analysis of {T}amil {X} ({T}witter) Comments Using Incremental Learning",
author = "S, Kalaivani K and
R, Sanjay and
M, Thissyakkanna S and
K, Nirenjhanram S",
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-1.38/",
doi = "10.18653/v1/2025.dravidianlangtech-1.38",
pages = "221--225",
ISBN = "979-8-89176-228-2",
abstract = "The introduction of Jio in India has significantly increased the number of social media users, particularly on platforms like X (Twitter), Facebook, Instagram. While this growth is positive, it has also led to a rise in native language speakers, making social media analysis more complex. In this study, we focus on Tamil, a Dravidian language, and aim to classify social media comments from X (Twitter) into seven different categories. Tamil speaking users often communicate using a mix of Tamil and English, creating unique challenges for analysis and tracking. This surge in diverse language usage on social media highlights the need for robust sentiment analysis tools to ensure the platform remains accessible and user-friendly for everyone with different political opinions. In this study we trained four machine learning models, SGD Classifier, Random Forest Classifier, Decision Tree, and Multinomial Naive Bayes classifier to identify and classify the comments. Among these, the SGD Classifier achieved the best performance, with a training accuracy of 83.67{\%} and a validation accuracy of 80.43{\%}."
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%0 Conference Proceedings
%T KSK@DravidianLangTech 2025: Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments Using Incremental Learning
%A S, Kalaivani K.
%A R, Sanjay
%A M, Thissyakkanna S.
%A K, Nirenjhanram S.
%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 s-etal-2025-ksk
%X The introduction of Jio in India has significantly increased the number of social media users, particularly on platforms like X (Twitter), Facebook, Instagram. While this growth is positive, it has also led to a rise in native language speakers, making social media analysis more complex. In this study, we focus on Tamil, a Dravidian language, and aim to classify social media comments from X (Twitter) into seven different categories. Tamil speaking users often communicate using a mix of Tamil and English, creating unique challenges for analysis and tracking. This surge in diverse language usage on social media highlights the need for robust sentiment analysis tools to ensure the platform remains accessible and user-friendly for everyone with different political opinions. In this study we trained four machine learning models, SGD Classifier, Random Forest Classifier, Decision Tree, and Multinomial Naive Bayes classifier to identify and classify the comments. Among these, the SGD Classifier achieved the best performance, with a training accuracy of 83.67% and a validation accuracy of 80.43%.
%R 10.18653/v1/2025.dravidianlangtech-1.38
%U https://aclanthology.org/2025.dravidianlangtech-1.38/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.38
%P 221-225
Markdown (Informal)
[KSK@DravidianLangTech 2025: Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments Using Incremental Learning](https://aclanthology.org/2025.dravidianlangtech-1.38/) (S et al., DravidianLangTech 2025)
ACL
- Kalaivani K S, Sanjay R, Thissyakkanna S M, and Nirenjhanram S K. 2025. KSK@DravidianLangTech 2025: Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments Using Incremental Learning. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 221–225, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.