@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://aclanthology.org/2025.dravidianlangtech-1.109/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T TensorTalk@DravidianLangTech 2025: Sentiment Analysis in Tamil and Tulu using Logistic Regression and SVM
%A Anishka, K.
%A J, Anne Jacika
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F anishka-j-2025-tensortalk
%X 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.
%R 10.18653/v1/2025.dravidianlangtech-1.109
%U https://aclanthology.org/2025.dravidianlangtech-1.109/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.109
%P 636-641
Markdown (Informal)
[TensorTalk@DravidianLangTech 2025: Sentiment Analysis in Tamil and Tulu using Logistic Regression and SVM](https://aclanthology.org/2025.dravidianlangtech-1.109/) (Anishka & J, DravidianLangTech 2025)
ACL