@inproceedings{roy-etal-2025-lexilogic-dravidianlangtech,
    title = "{L}exi{L}ogic@{D}ravidian{L}ang{T}ech 2025: Political Multiclass Sentiment Analysis of {T}amil {X}({T}witter) Comments and Sentiment Analysis in {T}amil and {T}ulu",
    author = "Roy, Billodal  and
      Bhattacharyya, Souvik  and
      Gupta, Pranav  and
      M, Niranjan Kumar",
    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/fix-sig-urls/2025.dravidianlangtech-1.96/",
    pages = "557--561",
    ISBN = "979-8-89176-228-2",
    abstract = "We present our approach and findings for two sentiment analysis shared tasks as part of DravidianLangTech@NAACL 2025. The first task involved a seven-class political sentiment classification for Tamil tweets, while the second addressed code-mixed sentiment analysis in Tamil-English and Tulu-English social media texts. We employed language-specific BERT models fine-tuned on the respective tasks, specifically utilizing the L3Cube-Tamil-BERT for Tamil classification and a Telugu-based BERT model for Tulu classification. Our system achieved notable results, particularly securing the first position in the Tulu code-mixed sentiment analysis track. The experiments demonstrate the effectiveness of language-specific pre-trained models for Dravidian language sentiment analysis, while also highlighting the challenges in handling political discourse and code-mixed content."
}