@inproceedings{adyanthaya-p-2025-yenlp,
    title = "{Y}en{LP}{\_}{CS}@{D}ravidian{L}ang{T}ech 2025: Sentiment Analysis on Code-Mixed {T}amil-{T}ulu Data Using Machine Learning and Deep Learning Models",
    author = "Adyanthaya, Raksha  and
      P, Rathnakara Shetty",
    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/ingest-emnlp/2025.dravidianlangtech-1.50/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.50",
    pages = "288--292",
    ISBN = "979-8-89176-228-2",
    abstract = "The sentiment analysis in code-mixed Dravidian languages such as Tamil-English and Tulu-English is the focus of this study because these languages present difficulties for conventional techniques. In this work, We used ensembles, multilingual Bidirectional Encoder Representation(mBERT), Bidirectional Long Short Term Memory (BiLSTM), Random Forest (RF), Support Vector Machine (SVM), and preprocessing in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec feature extraction. mBERT obtained accuracy of 64{\%} for Tamil and 68{\%} for Tulu on development datasets. In test sets, the ensemble model gave Tamil a macro F1-score of 0.4117, while mBERT gave Tulu a macro F1-score of 0.5511. With regularization and data augmentation, these results demonstrate the approach{'}s potential for further advancements."
}