@inproceedings{karunanidhi-arumugam-2026-chmod-777,
    title = "{CHMOD}{\_}777@{D}ravidian{L}ang{T}ech 2026: {LLM} Augmented Transformer Fine-tuning for {T}amil Political Sentiment Analysis",
    author = "Karunanidhi, Arunaggiri Pandian  and
      Arumugam, Prabalakshmi",
    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-1.23/",
    pages = "181--185",
    ISBN = "979-8-89176-401-9",
    abstract = "This paper describes Team CHMOD{\_}777{'}s system for the DravidianLangTech@ACL 2026 shared task on political multiclass sentiment analysis of Tamil Twitter comments. The task requires classifying Tamil political tweets into seven sentiment categories under severe class imbalance (8:1 ratio). We address this challenge through LLM-based data augmentation using Gemini 2.5 Flash, expanding training data from 4,352 to 15,316 samples (3.5x the original). Our best system, MuRIL fine-tuned on augmented data with Focal Loss (gamma=3.0) and weighted sampling, achieves 35.79{\%} Macro F1 on the development set, a 67{\%} relative improvement over the non-augmented baseline. On the official test set, our system achieves 34.25{\%} Macro F1, ranking 12th out of 22 participating teams. We find that (1) language-specific pre-training (MuRIL, 236M) outperforms larger general models (IndicBERT-v3, 1B), (2) smaller models benefit disproportionately from augmentation, and (3) Substantiated is the hardest category (F1=10.7{\%}) due to its requirement for factual reasoning."
}