  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. This deep learning model achieves an
accuracy of 71\% for 2-class sentiment classification compared to the current
state-of-the-art accuracy of 68\%. We also present the very first Bengali SA
classifier for the 3-class manually tagged dataset, and our proposed model
achieves an accuracy of 60\%. We further use this model to analyze the
sentiment of public comments in the online daily newspaper. Our analysis shows
that people post negative comments for political or sports news more often,
while the religious article comments represent positive sentiment. The dataset
and code is publicly available at
https://github.com/KhondokerIslam/Bengali\_Sentiment.
