  Sentiment Analysis (SA) refers to the task of associating a view polarity
(usually, positive, negative, or neutral; or even fine-grained such as slightly
angry, sad, etc.) to a given text, essentially breaking it down to a supervised
(since we have the view labels apriori) classification task. Although heavily
studied in resource-rich languages such as English thus pushing the SOTA by
leaps and bounds, owing to the arrival of the Transformer architecture, the
same cannot be said for resource-poor languages such as Bengali (BN). For a
language spoken by roughly 300 million people, the technology enabling them to
run trials on their favored tongue is severely lacking. In this paper, we
analyze the SOTA for SA in Bengali, particularly, Transformer-based models. We
discuss available datasets, their drawbacks, the nuances associated with
Bengali i.e. what makes this a challenging language to apply SA on, and finally
provide insights for future direction to mitigate the limitations in the field.
