The Role of Sentiment Analysis and Transformer Models in Stock Market Price Forecasting

Stock market analysis is one of the most important, to understand the dynamics of stock movements. This includes utilizing the available data and analyzing past time series data for making the decisions. The forecasting stock prices is a daunting task due to various number of diverse factors. In order to apply sentiment analysis techniques, these factors should have considered carefully for more accurate predictions. Now-a-days there are more number of prediction methodologies which may be used independently or as a hybrid approach. Amid these machine learning techniques have been revealed good performances in the prediction process. The objective of this research is to examine the utilization of transformer models in order to perform sentiment analysis and prediction of stock market. This review highlights the current state in the field of stock market prediction in the discovery of the research gaps, the suitable methodologies, and challenges, along with future directions. This paper is useful to researchers and practitioners in this direction towards more robust and accurate predictions of stock markets.