Transfer Learning and Transformer Architecture for Financial Sentiment Analysis
This work addresses sentiment analysis for financial institutions to improve credit scoring, but it appears incremental as it builds on existing transfer learning and transformer methods.
The paper tackled financial sentiment analysis by proposing a pre-trained language model that leverages transfer learning and transformer architecture, achieving results with fewer labeled data, though no concrete numbers are provided.
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.