Sentiment analysis in Bengali via transfer learning using multi-lingual BERT

Understand

Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns.

• The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder.
• In this paper, we present manually tagged 2-class and 3-class SA datasets in Bengali.
• We also demonstrate that the multi-lingual BERT model with relevant extensions can be trained via the approach of transfer learning over those novel datasets to improve the state-of-the-art performance in sentiment classification tasks.