@inproceedings{p-etal-2025-codecrackers,
    title = "codecrackers@{D}ravidian{L}ang{T}ech 2025: Sentiment Classification in {T}amil and {T}ulu Code-Mixed Social Media Text Using Machine Learning",
    author = "P, Lalith Kishore V  and
      Manikandan, Dr G  and
      A, Mohan Raj M  and
      A, Keerthi Vasan  and
      M, Aravindh",
    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/corrections-2025-06/2025.dravidianlangtech-1.69/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.69",
    pages = "387--391",
    ISBN = "979-8-89176-228-2",
    abstract = "Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the ``Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu'', which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings."
}