Abstract
In the real world, there are a variety of situations that require strategy control, that is reinforcement learning, as a method for studying the decision-making and behavioral strategies of intelligence. It has received a lot of research and empirical evidence on its functions and roles and is also a method recognized by scholars. Among them, combining reinforcement learning with sentiment analysis is an important theoretical research direction, but so far there is still relatively little research work about it, and it still has the problems of poor application effect and low accuracy rate. Therefore, in this study, we use the features related to sentiment analysis and deep reinforcement learning and use various algorithms for optimization to deal with the above problems. In this study, a sentiment analysis method incorporating knowledge graphs is designed using the characteristics of the stock trading market. A deep reinforcement learning investment trading strategy algorithm for sentiment analysis combined with knowledge graphs from this study is used in the subsequent experiments. The deep reinforcement learning system combining sentiment analysis and knowledge graph implemented in this study not only analyzes the algorithm from the theoretical aspect but also simulates data from the stock exchange market for experimental comparison and analysis. The experimental results illustrate that the deep reinforcement learning algorithm combining sentiment analysis and knowledge graphs used in this study can achieve better gains than the existing traditional reinforcement learning algorithms and has better practical application value.
Highlights
Reinforcement learning, as a subset of machine learning, which in turn is an important branch of artificial intelligence, has gained more and more importance in the last years [2]. e classical approach to creating AI requires programmers to manually code every rule that defines the behavior of the software [3]
Our work is a study of stock market sentiment analysis and investment strategy algorithms based on deep reinforcement learning, which first uses knowledge mapping techniques to improve the relevance of news headlines, uses them in sentiment analysis to derive the sentiment coefficients of the corresponding stocks for each news item, and applies them to a modified deep recurrent Q-learning network (DRQN) to find the optimal trading strategy in a complex and dynamic stock market
E accuracy of KGRCNN is above 86% in the best combination of hyperparameters. e experiments comparing the model in this study with other methods reveal that the KGRCNN method has a great advantage over the common LR and SVM models, while the recurrent convolutional neural network (RCNN) model can classify the stock market sentiment better and using a convolutional neural network (CNN) or recurrent neural network (RNN) approach does not affect the results and the EPS EPS
Summary
Reinforcement learning is a commonly used framework as a way to handle sequential decision-making tasks. While it is true that traditional finance is not at the forefront of adopting machine learning, its use in finance is a hit It offers new technological services for financial forecasting, customer service, and data security. Our work is a study of stock market sentiment analysis and investment strategy algorithms based on deep reinforcement learning, which first uses knowledge mapping techniques to improve the relevance of news headlines, uses them in sentiment analysis to derive the sentiment coefficients of the corresponding stocks for each news item, and applies them to a modified deep recurrent Q-learning network (DRQN) to find the optimal trading strategy in a complex and dynamic stock market
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
