@inproceedings{subramanian-etal-2025-kec-elite,
    title = "{KEC}-Elite-Analysts@{D}ravidian{L}ang{T}ech 2025: Deciphering Emotions in {T}amil-{E}nglish and Code-Mixed Social Media Tweets",
    author = "Subramanian, Malliga  and
      A, Aruna  and
      T, Anbarasan  and
      M, Amudhavan  and
      S, Jahaganapathi  and
      Shanmugavadivel, Kogilavani",
    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-1.52/",
    pages = "299--303",
    ISBN = "979-8-89176-228-2",
    abstract = "Sentiment analysis in code-mixed languages, particularly Tamil-English, is a growing challenge in natural language processing (NLP) due to the prevalence of multilingual communities on social media. This paper explores various machine learning and transformer-based models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), BERT, and mBERT, for sentiment classification of Tamil-English code-mixed text. The models are evaluated on a shared task dataset provided by DravidianLangTech@NAACL 2025, with performance measured through accuracy, precision, recall, and F1-score. Our results demonstrate that transformer-based models, particularly mBERT, outperform traditional classifiers in identifying sentiment polarity. Future work aims to address the challenges posed by code-switching and class imbalance through advanced model architectures and data augmentation techniques."
}