YenLP_CS@DravidianLangTech 2025: Sentiment Analysis on Code-Mixed Tamil-Tulu Data Using Machine Learning and Deep Learning Models

AbstractThe sentiment analysis in code-mixed Dravidian languages such as Tamil-English and Tulu-English is the focus of this study because these languages present difficulties for conventional techniques. In this work, We used ensembles, multilingual Bidirectional Encoder Representation(mBERT), Bidirectional Long Short Term Memory (BiLSTM), Random Forest (RF), Support Vector Machine (SVM), and preprocessing in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec feature extraction. mBERT obtained accuracy of 64% for Tamil and 68% for Tulu on development datasets. In test sets, the ensemble model gave Tamil a macro F1-score of 0.4117, while mBERT gave Tulu a macro F1-score of 0.5511. With regularization and data augmentation, these results demonstrate the approach’s potential for further advancements.