Tadesse Kebede
2023
Habesha@DravidianLangTech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis.
Mesay Gemeda Yigezu
|
Tadesse Kebede
|
Olga Kolesnikova
|
Grigori Sidorov
|
Alexander Gelbukh
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.
Search