@inproceedings{bhuvana-etal-2025-ssntrio-dravidianlangtech,
title = "{SSNT}rio@{D}ravidian{L}ang{T}ech 2025: Sentiment Analysis in {D}ravidian Languages using Multilingual {BERT}",
author = "Bhuvana, J and
T T, Mirnalinee and
Seshan, Diya and
R, Rohan and
Koushik, Avaneesh",
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-1.60/",
doi = "10.18653/v1/2025.dravidianlangtech-1.60",
pages = "340--344",
ISBN = "979-8-89176-228-2",
abstract = "This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not{\_}tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed{\_}feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task."
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<abstract>This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not_tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed_feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task.</abstract>
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%0 Conference Proceedings
%T SSNTrio@DravidianLangTech 2025: Sentiment Analysis in Dravidian Languages using Multilingual BERT
%A Bhuvana, J.
%A T T, Mirnalinee
%A Seshan, Diya
%A R, Rohan
%A Koushik, Avaneesh
%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 bhuvana-etal-2025-ssntrio-dravidianlangtech
%X This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not_tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed_feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task.
%R 10.18653/v1/2025.dravidianlangtech-1.60
%U https://aclanthology.org/2025.dravidianlangtech-1.60/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.60
%P 340-344
Markdown (Informal)
[SSNTrio@DravidianLangTech 2025: Sentiment Analysis in Dravidian Languages using Multilingual BERT](https://aclanthology.org/2025.dravidianlangtech-1.60/) (Bhuvana et al., DravidianLangTech 2025)
ACL
- J Bhuvana, Mirnalinee T T, Diya Seshan, Rohan R, and Avaneesh Koushik. 2025. SSNTrio@DravidianLangTech 2025: Sentiment Analysis in Dravidian Languages using Multilingual BERT. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 340–344, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.