@inproceedings{s-etal-2026-tamilecho,
    title = "{T}amil{E}cho{\_}{P}olitical@{D}ravidian{L}ang{T}ech 2026: Hybrid {XLM}-{R}o{BERT}a with Sarcasm-Aware Feature Fusion for Political Multiclass Sentiment Analysis in {T}amil {X}",
    author = "S, Kanimozhi Selvi C  and
      S, Inigashree N  and
      J, Kavinraj  and
      G, Moneissh A",
    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-1.61/",
    pages = "387--391",
    ISBN = "979-8-89176-401-9",
    abstract = "Political sentiment analysis in Tamil social media is challenging due to informal language, sarcasm, emoji-driven sentiment inversion, and severe class imbalance. This paper presents TamilEcho, our system submitted to the Shared Task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments at DravidianLangTech@ACL 2026. We propose a hybrid architecture that integrates contextual representations from XLM-RoBERTa with lexical TF-IDF features and explicit sarcasm-aware emoji features. Domain-specific hashtag expansion is incorporated to enrich political context. To address class imbalance, we apply inverse-frequency class weighting and label smoothing during training. Experimental results demonstrate that hybrid feature fusion significantly improves performance over transformer-only baselines. Our final system achieves a Macro-F1 score of 0.3559 on the official test set, securing Rank 10 among participating teams. The results highlight the effectiveness of combining semantic, lexical, and pragmatic cues for fine-grained political sentiment classification in Tamil."
}