@inproceedings{durairaj-etal-2025-overview,
title = "Overview of the Shared Task on Sentiment Analysis in {T}amil and {T}ulu",
author = "Thenmozhi, Durairaj and
Chakravarthi, Bharathi Raja and
Hegde, Asha and
Shashirekha, Hosahalli Lakshmaiah and
Natarajan, Rajeswari and
Thavareesan, Sajeetha and
Sakuntharaj, Ratnasingam and
Kalyanasundaram, Krishnakumari and
Rajkumar, Charmathi and
Shetty, Poorvi and
Kumar, Harshitha S",
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-1.124/",
doi = "10.18653/v1/2025.dravidianlangtech-1.124",
pages = "732--738",
ISBN = "979-8-89176-228-2",
abstract = "Sentiment analysis is an essential task for interpreting subjective opinions and emotions in textual data, with significant implications across commercial and societal applications. This paper provides an overview of the shared task on Sentiment Analysis in Tamil and Tulu, organized as part of DravidianLangTech@NAACL 2025. The task comprises two components: one addressing Tamil and the other focusing on Tulu, both designed as multi-class classification challenges, wherein the sentiment of a given text must be categorized as positive, negative, neutral and unknown. The dataset was diligently organized by aggregating user-generated content from social media platforms such as YouTube and Twitter, ensuring linguistic diversity and real-world applicability. Participants applied a variety of computational approaches, ranging from classical machine learning algorithms such as Traditional Machine Learning Models, Deep Learning Models, Pre-trained Language Models and other Feature Representation Techniques to tackle the challenges posed by linguistic code-mixing, orthographic variations, and resource scarcity in these low resource languages."
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<abstract>Sentiment analysis is an essential task for interpreting subjective opinions and emotions in textual data, with significant implications across commercial and societal applications. This paper provides an overview of the shared task on Sentiment Analysis in Tamil and Tulu, organized as part of DravidianLangTech@NAACL 2025. The task comprises two components: one addressing Tamil and the other focusing on Tulu, both designed as multi-class classification challenges, wherein the sentiment of a given text must be categorized as positive, negative, neutral and unknown. The dataset was diligently organized by aggregating user-generated content from social media platforms such as YouTube and Twitter, ensuring linguistic diversity and real-world applicability. Participants applied a variety of computational approaches, ranging from classical machine learning algorithms such as Traditional Machine Learning Models, Deep Learning Models, Pre-trained Language Models and other Feature Representation Techniques to tackle the challenges posed by linguistic code-mixing, orthographic variations, and resource scarcity in these low resource languages.</abstract>
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%0 Conference Proceedings
%T Overview of the Shared Task on Sentiment Analysis in Tamil and Tulu
%A Thenmozhi, Durairaj
%A Chakravarthi, Bharathi Raja
%A Hegde, Asha
%A Shashirekha, Hosahalli Lakshmaiah
%A Natarajan, Rajeswari
%A Thavareesan, Sajeetha
%A Sakuntharaj, Ratnasingam
%A Kalyanasundaram, Krishnakumari
%A Rajkumar, Charmathi
%A Shetty, Poorvi
%A Kumar, Harshitha S.
%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 durairaj-etal-2025-overview
%X Sentiment analysis is an essential task for interpreting subjective opinions and emotions in textual data, with significant implications across commercial and societal applications. This paper provides an overview of the shared task on Sentiment Analysis in Tamil and Tulu, organized as part of DravidianLangTech@NAACL 2025. The task comprises two components: one addressing Tamil and the other focusing on Tulu, both designed as multi-class classification challenges, wherein the sentiment of a given text must be categorized as positive, negative, neutral and unknown. The dataset was diligently organized by aggregating user-generated content from social media platforms such as YouTube and Twitter, ensuring linguistic diversity and real-world applicability. Participants applied a variety of computational approaches, ranging from classical machine learning algorithms such as Traditional Machine Learning Models, Deep Learning Models, Pre-trained Language Models and other Feature Representation Techniques to tackle the challenges posed by linguistic code-mixing, orthographic variations, and resource scarcity in these low resource languages.
%R 10.18653/v1/2025.dravidianlangtech-1.124
%U https://aclanthology.org/2025.dravidianlangtech-1.124/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.124
%P 732-738
Markdown (Informal)
[Overview of the Shared Task on Sentiment Analysis in Tamil and Tulu](https://aclanthology.org/2025.dravidianlangtech-1.124/) (Thenmozhi et al., DravidianLangTech 2025)
ACL
- Durairaj Thenmozhi, Bharathi Raja Chakravarthi, Asha Hegde, Hosahalli Lakshmaiah Shashirekha, Rajeswari Natarajan, Sajeetha Thavareesan, Ratnasingam Sakuntharaj, Krishnakumari Kalyanasundaram, Charmathi Rajkumar, Poorvi Shetty, and Harshitha S Kumar. 2025. Overview of the Shared Task on Sentiment Analysis in Tamil and Tulu. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 732–738, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.