@inproceedings{k-etal-2025-wictory,
    title = "Wictory@{D}ravidian{L}ang{T}ech 2025: Political Sentiment Analysis of {T}amil {X}({T}witter) Comments using {L}a{BSE} and {SVM}",
    author = "K, Nithish Ariyha  and
      R, Eshwanth Karti T  and
      P, Yeshwanth Balaji A  and
      J, Vikash  and
      S, Sachin Kumar",
    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-1.120/",
    pages = "706--710",
    ISBN = "979-8-89176-228-2",
    abstract = "Political sentiment analysis has become an essential area of research in Natural Language Processing (NLP), driven by the rapid rise ofsocial media as a key platform for political discourse. This study focuses on sentiment classification in Tamil political tweets, addressing the linguistic and cultural complexities inherent in low-resource languages. To overcome data scarcity challenges, we develop a system that integrates embeddings with advanced Machine Learning techniques, ensuring effective sentiment categorization. Our approach leverages deep learning-based models and transformer architectures to capture nuanced expressions, contributing to improved sentiment classification. This work enhances NLP methodologies for low-resource languages and provides valuable insights into Tamil political discussions, aiding policymakers and researchers in understanding public sentiment more accurately. Notably, our system secured $Rank 5$in the NAACL shared task, demonstrating its effectiveness in real-world sentiment classification challenges."
}