@inproceedings{vegupatti-etal-2026-tamilpolisent,
    title = "{T}amil{P}oli{S}ent 2026: A Shared Task report on Multiclass Political Sentiment Analysis in {T}amil",
    author = "Vegupatti, Mani  and
      Ponnusamy, Kishore Kumar  and
      Chakravarthi, Bharathi Raja  and
      Rajiakodi, Saranya  and
      Durairaj, Thenmozhi  and
      Kumaresan, Prasanna Kumar  and
      Thangasamy, Sathiyaraj",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.15/",
    pages = "134--143",
    ISBN = "979-8-89176-401-9",
    abstract = "Political sentiment analysis aims to automatically identify opinions and attitudes expressed in political discourse on social media platforms. This paper presents an overview of the TamilPoliSent 2026 shared task on multiclass political sentiment analysis in Tamil, organized as part of DravidianLangTech@ACL 2026. The task focuses on categorizing Tamil comments from X (formerly Twitter) into seven sentiment classes: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. The dataset consists of 5,440 annotated Tamil tweets collected from political discussions on social media. Participants were provided with labeled training and development datasets, while the test set was used for final evaluation.A total of 22 teams participated in the shared task and explored a wide range of modeling approaches including classical machine learning methods, transformer-based architectures, hybrid lexical{--}contextual models, and ensemble frameworks. System performance was evaluated using Macro F1-score to ensure balanced evaluation across all sentiment categories. The best-performing system achieved a Macro F1-score of 0.3935.The results highlight several challenges in Tamil political sentiment analysis, including class imbalance, sarcasm, informal writing styles, and semantic overlap between sentiment categories. The shared task demonstrates that transformer-based models combined with class-balanced learning and hybrid representations are effective for handling fine-grained political sentiment classification in low-resource languages. These findings contribute to advancing research in political discourse analysis and natural language processing for Tamil and other under-resourced languages."
}