A Review on Sentiment Analysis in Reinforcement Learning Model for Stock Market Analysis

Abstract

Sentiment analysis is a natural language processing approach that is widely implemented for many natural language processing applications such as translation, chatbots, and more. In this paper, news sentiment analysis in the stock market was reviewed. Stock market sentiment fluctuates because of events such as the reporting of quarterly financial report, macroeconomic data, government policy, etc. Traders use sentiment analysis to predict stock prices and trends. In recent years, sentiment analysis has been embedded into reinforcement learning for building an algorithmic trading system. This paper is a review paper to analyze and summarize sentiment analysis in reinforcement learning models. Specifically, the paper reviews the data, features, and approaches used in sentiment analysis. To identify the relevant journals and articles, a methodology is applied to review the sentiment analysis in the stock market. These studies are categorized according to their general similarities, differences, limitations, and field to be investigated further. Finally, the last section is the conclusion, which provides the direction for future study.