@inproceedings{c-etal-2026-trailblazer,
    title = "Trailblazer@{D}ravidian{L}ang{T}ech 2026: A Comparative Study of {TF}-{IDF} {SVM} and {XLM}-{R}o{BERT}a for Political Multiclass Text Classification.",
    author = "C, Anuradha  and
      R, Anbuaruvi  and
      Murugan, Shanthi",
    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-1.66/",
    pages = "414--419",
    ISBN = "979-8-89176-401-9",
    abstract = "The rapid growth of social media networks faces challenges in the classification of multilingual and code-mixed data. A task is shared by Political Multiclass Sentiment Analysis of Tamil X (Twitter) -DravidianLangTech@ACL 2026 to classify the political text.For the above task, we proposed solutions to compare a traditional machine learning and the transformer based model. First we developed a Baseline traditional Support vector Machine model using the TF-IDF features. To provide a stronger Indic-language baseline we consider the IndicBERT, a transformer model specifically designed for Indian Languages. IndicBERT improves contextual understanding of Tamil-English code-mixed political text . To capture the deeper information from the text we developed an XLM-RoBERTa model where we used minimal pre-processing technique. The Result shows us that the transformer-based performs well compared to the traditional baseline model with the macro F1 score of 0.3738. The Study highlights the importance of robust multi-class social media political text classification."
}