@inproceedings{krasitskii-etal-2025-advancing,
    title = "Advancing Sentiment Analysis in {T}amil-{E}nglish Code-Mixed Texts: Challenges and Transformer-Based Solutions",
    author = "Krasitskii, Mikhail  and
      Kolesnikova, Olga  and
      Chanona Hernandez, Liliana  and
      Sidorov, Grigori  and
      Gelbukh, Alexander",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nlp4dh-1.27/",
    pages = "305--312",
    ISBN = "979-8-89176-234-3",
    abstract = "This study examines sentiment analysis in Tamil-English code-mixed texts using advanced transformer-based architectures. The unique linguistic challenges, including mixed grammar, orthographic variability, and phonetic inconsistencies, are addressed. Data limitations and annotation gaps are discussed, highlighting the need for larger datasets. The performance of models such as XLM-RoBERTa, mT5, IndicBERT, and RemBERT is evaluated, with insights into their optimization for low-resource, code-mixed environments."
}