@inproceedings{nguyen-van-2026-phucnguyen,
    title = "{P}huc{N}guyen@{D}ravidian{L}ang{T}ech 2026: Political Multiclass Sentiment Analysis with {XLM}-{R}o{BERT}a and Low-Rank Adaptation",
    author = "Nguyen, Dinh Khac Phuc  and
      V{\u{a}}n, Th{\`i}n {\DJ}ặng",
    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-1.49/",
    pages = "321--325",
    ISBN = "979-8-89176-401-9",
    abstract = "Analyzing political sentiment in code-mixed Tamil-English presents significant challenges due to informal jargon, severe class imbalance, and distribution shifts. This paper describes our system for the Political Multiclass Sentiment Analysis shared task at DravidianLangTech@ACL 2026, which categorizes tweets into seven sentiment classes. Our approach leverages XLM-RoBERTa integrated with Low-Rank Adaptation (LoRA). To mitigate majority-class dominance, we combine random oversampling with automated hyperparameter optimization to improve macro-level balance within this Parameter-Efficient Fine-Tuning (PEFT) framework. Enhanced by targeted preprocessing{---}specifically emoji demojization and noise removal{---}our system helps preserve nuanced symbolic cues, achieving a macro-average F1-score of 0.3763 and securing Rank 2 on the shared task leaderboard."
}