@inproceedings{anishka-j-2025-tensortalk,
    title = "{T}ensor{T}alk@{D}ravidian{L}ang{T}ech 2025: Sentiment Analysis in {T}amil and {T}ulu using Logistic Regression and {SVM}",
    author = "Anishka, K  and
      J, Anne Jacika",
    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-1.109/",
    pages = "636--641",
    ISBN = "979-8-89176-228-2",
    abstract = "Words are powerful; they shape thoughts that influence actions and reveal emotions. On social media, where billions of people share theiropinions daily. Comments are the key to understanding how users feel about a video, an image, or even an idea. But what happens when these comments are messy, riddled with code-mixed language, emojis, and informal text? The challenge becomes even greater when analyzing low-resource languages like Tamil and Tulu. To tackle this, TensorTalk deployed cutting-edge machine learning techniques such as Logistic regression for Tamil language and SVM for Tulu language , to breathe life into unstructured data. By balancing, cleaning, and processing comments, TensorTalk broke through barriers like transliteration and tokenization, unlocking the emotions buried in the language."
}