@inproceedings{gowda-etal-2025-bridging,
    title = "Bridging Linguistic Complexity: Sentiment Analysis of {T}amil Code-Mixed Text Using Meta-Model",
    author = "Gowda, Anusha M D  and
      Vikram, Deepthi  and
      Hegde, Parameshwar R",
    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-1.18/",
    pages = "103--108",
    ISBN = "979-8-89176-228-2",
    abstract = "Sentiment analysis in code-mixed languages poses significant challenges due to the complex nature of mixed-language text. This study explores sentiment analysis on Tamil code-mixed text using deep learning models such as Long Short-Term Memory (LSTM), hybrid models like Convolutional Neural Network (CNN) + Gated Recurrent Unit (GRU) and LSTM + GRU, along with meta-models including Logistic Regression, Random Forest, and Decision Tree. The LSTM+GRU hybrid model achieved an accuracy of 0.31, while the CNN+GRU hybrid model reached 0.28. The Random Forest meta-model demonstrated exceptional performance on the development set with an accuracy of 0.99. However, its performance dropped significantly on the test set, achieving an accuracy of 0.1333. The study results emphasize the potential of meta-model-based classification for improving performance in NLP tasks."
}