MUNLP@DravidianLangTech2023: Learning Approaches for Sentiment Analysis in Code-mixed Tamil and Tulu Text

Asha Hegde, Kavya G, Sharal Coelho, Pooja Lamani, Hosahalli Lakshmaiah Shashirekha

Abstract
Sentiment Analysis (SA) examines the subjective content of a statement, such as opinions, assessments, feelings, or attitudes towards a subject, person, or a thing. Though several models are developed for SA in high-resource languages like English, Spanish, German, etc., uder-resourced languages like Dravidian languages are less explored. To address the challenges of SA in low resource Dravidian languages, in this paper, we team MUNLP describe the models submitted to “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)-2023. n-gramsSA, EmbeddingsSA and BERTSA are the models proposed for SA shared task. Among all the models, BERTSA exhibited a maximum macro F1 score of 0.26 for code-mixed Tamil texts securing 2nd place in the shared task. EmbeddingsSA exhibited maximum macro F1 score of 0.53 securing 2nd place for Tulu code-mixed texts.
Anthology ID:
2023.dravidianlangtech-1.40
Volume:
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
Month:
September
Year:
2023
Address:
Varna, Bulgaria
Editors:
Bharathi R. Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Sajeetha Thavareesan, Elizabeth Sherly
Venues:
DravidianLangTech | WS
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
275–281
Language:
URL:
https://aclanthology.org/2023.dravidianlangtech-1.40
DOI:
Bibkey:
Cite (ACL):
Asha Hegde, Kavya G, Sharal Coelho, Pooja Lamani, and Hosahalli Lakshmaiah Shashirekha. 2023. MUNLP@DravidianLangTech2023: Learning Approaches for Sentiment Analysis in Code-mixed Tamil and Tulu Text. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 275–281, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
MUNLP@DravidianLangTech2023: Learning Approaches for Sentiment Analysis in Code-mixed Tamil and Tulu Text (Hegde et al., DravidianLangTech-WS 2023)
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