  Financial sentiment analysis allows financial institutions like Banks and
Insurance Companies to better manage the credit scoring of their customers in a
better way. Financial domain uses specialized mechanisms which makes sentiment
analysis difficult. In this paper, we propose a pre-trained language model
which can help to solve this problem with fewer labelled data. We extend on the
principles of Transfer learning and Transformation architecture principles and
also take into consideration recent outbreak of pandemics like COVID. We apply
the sentiment analysis to two different sets of data. We also take smaller
training set and fine tune the same as part of the model.
