Zahra Ahani
2023
LIDOMA@DravidianLangTech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in Tamil and Tulu Languages
Moein Tash
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Jesus Armenta-Segura
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Zahra Ahani
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Olga Kolesnikova
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Grigori Sidorov
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Alexander Gelbukh
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively
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