  Due to the breathtaking growth of social media or newspaper user comments,
online product reviews comments, sentiment analysis (SA) has captured
substantial interest from the researchers. With the fast increase of domain, SA
work aims not only to predict the sentiment of a sentence or document but also
to give the necessary detail on different aspects of the sentence or document
(i.e. aspect-based sentiment analysis). A considerable number of datasets for
SA and aspect-based sentiment analysis (ABSA) have been made available for
English and other well-known European languages. In this paper, we present a
manually annotated Bengali dataset of high quality, BAN-ABSA, which is
annotated with aspect and its associated sentiment by 3 native Bengali
speakers. The dataset consists of 2,619 positive, 4,721 negative and 1,669
neutral data samples from 9,009 unique comments gathered from some famous
Bengali news portals. In addition, we conducted a baseline evaluation with a
focus on deep learning model, achieved an accuracy of 78.75% for aspect term
extraction and accuracy of 71.08% for sentiment classification. Experiments on
the BAN-ABSA dataset show that the CNN model is better in terms of accuracy
though Bi-LSTM significantly outperforms CNN model in terms of average
F1-score.
