Charmathi Rajkumar


2023

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VEL@DravidianLangTech: Sentiment Analysis of Tamil and Tulu
Kishore Kumar Ponnusamy | Charmathi Rajkumar | Prasanna Kumar Kumaresan | Elizabeth Sherly | Ruba Priyadharshini
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.