@inproceedings{subramanian-etal-2025-kec-tech,
    title = "{KEC}{\_}{TECH}{\_}{TITANS}@{D}ravidian{L}ang{T}ech 2025:Sentiment Analysis for Low-Resource Languages: Insights from {T}amil and {T}ulu using Deep Learning and Machine Learning Models",
    author = "Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      S, Dharshini  and
      P, Deepiga  and
      C, Praveenkumar  and
      S, Ananthakumar",
    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/landing_page/2025.dravidianlangtech-1.48/",
    pages = "278--282",
    ISBN = "979-8-89176-228-2",
    abstract = "Sentiment analysis in Dravidian languages like Tamil and Tulu presents significant challenges due to their linguistic diversity and limited resources for natural language processing (NLP). This study explores sentiment classification for Tamil and Tulu, focusing on the complexities of handling both languages, which differ in script, grammar, and vocabulary. We employ a variety of machine learning and deep learning techniques, including traditional models like Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), as well as advanced transformer-based models like BERT and multilingual BERT (mBERT). A key focus of this research is to evaluate the performance of these models on sentiment analysis tasks, considering metrics such as accuracy, precision, recall, and F1-score. The results show that transformer-based models, particularly mBERT, significantly outperform traditional machine learning models in both Tamil and Tulu sentiment classification. This study also highlights the need for further research into addressing challenges like language-specific nuances, dataset imbalance, and data augmentation techniques for improved sentiment analysis in under-resourced languages like Tamil and Tulu."
}