CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based Approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Texts

AbstractSentiment analysis (SA) on social media reviews has become a challenging research agenda in recent years due to the exponential growth of textual content. Although several effective solutions are available for SA in high-resourced languages, it is considered a critical problem for low-resourced languages. This work introduces an automatic system for analyzing sentiment in Tamil and Tulu code-mixed languages. Several ML (DT, RF, MNB), DL (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLM-RoBERTa, m-BERT) are investigated for SA tasks using Tamil and Tulu code-mixed textual data. Experimental outcomes reveal that the transformer-based models XLM-R and m-BERT surpassed others in performance for Tamil and Tulu, respectively. The proposed XLM-R and m-BERT models attained macro F1-scores of 0.258 (Tamil) and 0.468 (Tulu) on test datasets, securing the 2nd and 5th positions, respectively, in the shared task.