@inproceedings{mia-etal-2025-kcrl,
    title = "{KCRL}@{D}ravidian{L}ang{T}ech 2025: Multi-View Feature Fusion with {XLM}-{R} for {T}amil Political Sentiment Analysis",
    author = "Mia, Md Ayon  and
      Haq, Fariha  and
      Shawon, Md. Tanvir Ahammed  and
      Md. Mursalin, Golam Sarwar  and
      Khan, Muhammad Ibrahim",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.dravidianlangtech-1.108/",
    pages = "630--635",
    ISBN = "979-8-89176-228-2",
    abstract = "Political discourse on social media platforms significantly influences public opinion, necessitating accurate sentiment analysis for understanding societal perspectives. This paper presents a system developed for the shared task of Political Multiclass Sentiment Analysis in Tamil tweets. The task aims to classify tweets into seven distinct sentiment categories: Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None of the above. We propose a Multi-View Feature Fusion (MVFF) architecture that leverages XLM-R with a CLS-Attention-Mean mechanism for sentiment classification. Our experimental results demonstrate the effectiveness of our approach, achieving a macro-average F1-score of 0.37 on the test set and securing the 2nd position in the shared task. Through comprehensive error analysis, we identify specific classification challenges and demonstrate how our model effectively navigates the linguistic complexities of Tamil political discourse while maintaining robust classification performance across multiple sentiment categories."
}