@inproceedings{subramanian-etal-2025-kec-elite,
title = "{KEC}-Elite-Analysts@{D}ravidian{L}ang{T}ech 2025: Deciphering Emotions in {T}amil-{E}nglish and Code-Mixed Social Media Tweets",
author = "Subramanian, Malliga and
A, Aruna and
T, Anbarasan and
M, Amudhavan and
S, Jahaganapathi and
Shanmugavadivel, Kogilavani",
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-1.52/",
doi = "10.18653/v1/2025.dravidianlangtech-1.52",
pages = "299--303",
ISBN = "979-8-89176-228-2",
abstract = "Sentiment analysis in code-mixed languages, particularly Tamil-English, is a growing challenge in natural language processing (NLP) due to the prevalence of multilingual communities on social media. This paper explores various machine learning and transformer-based models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), BERT, and mBERT, for sentiment classification of Tamil-English code-mixed text. The models are evaluated on a shared task dataset provided by DravidianLangTech@NAACL 2025, with performance measured through accuracy, precision, recall, and F1-score. Our results demonstrate that transformer-based models, particularly mBERT, outperform traditional classifiers in identifying sentiment polarity. Future work aims to address the challenges posed by code-switching and class imbalance through advanced model architectures and data augmentation techniques."
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%0 Conference Proceedings
%T KEC-Elite-Analysts@DravidianLangTech 2025: Deciphering Emotions in Tamil-English and Code-Mixed Social Media Tweets
%A Subramanian, Malliga
%A A, Aruna
%A T, Anbarasan
%A M, Amudhavan
%A S, Jahaganapathi
%A Shanmugavadivel, Kogilavani
%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 subramanian-etal-2025-kec-elite
%X Sentiment analysis in code-mixed languages, particularly Tamil-English, is a growing challenge in natural language processing (NLP) due to the prevalence of multilingual communities on social media. This paper explores various machine learning and transformer-based models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), BERT, and mBERT, for sentiment classification of Tamil-English code-mixed text. The models are evaluated on a shared task dataset provided by DravidianLangTech@NAACL 2025, with performance measured through accuracy, precision, recall, and F1-score. Our results demonstrate that transformer-based models, particularly mBERT, outperform traditional classifiers in identifying sentiment polarity. Future work aims to address the challenges posed by code-switching and class imbalance through advanced model architectures and data augmentation techniques.
%R 10.18653/v1/2025.dravidianlangtech-1.52
%U https://aclanthology.org/2025.dravidianlangtech-1.52/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.52
%P 299-303
Markdown (Informal)
[KEC-Elite-Analysts@DravidianLangTech 2025: Deciphering Emotions in Tamil-English and Code-Mixed Social Media Tweets](https://aclanthology.org/2025.dravidianlangtech-1.52/) (Subramanian et al., DravidianLangTech 2025)
ACL
- Malliga Subramanian, Aruna A, Anbarasan T, Amudhavan M, Jahaganapathi S, and Kogilavani Shanmugavadivel. 2025. KEC-Elite-Analysts@DravidianLangTech 2025: Deciphering Emotions in Tamil-English and Code-Mixed Social Media Tweets. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 299–303, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.