@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://preview.aclanthology.org/landing_page/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."
}