A Stock Market Trend Prediction Method Based On Social Platform Public Opinion And Historical Price

For the past few years,the booming development of data mining and natural lan-guage processing technology make it possible to learn the relationship between social platform data and financial market fluctuations.The integration of natural language processing,deep learning technology and financial field is the general trend,and the task of stock market trend prediction is one of the hot topics.However,there are many factors affecting the movement of stock market,and its high volatility makes it a difficult problem to predict the stock market trend.The exist-ing research shows that the public opinion data of social platform affect or reflect the stock movement to a certain extent.To address the problem,this paper proposes a stock market prediction method based on public opinion text embedding and historical price.Firstly,different from the traditional text embedding methods,the text embedding method proposed in this paper not only takes the semantic characteristics and emotional factors of the text into account,but also considers the unique interactive structure char-acteristics of the public opinion text,so that the generated embedding vector represent-ing the public opinion text can contain more effective information.Specifically,this paper designs a public opinion node model to describe the potential connection about semantics and interaction characteristics in public opinion data by constructing a public opinion node network firstly,and then the model uses the graph embedding method to get the text representation from the network.In addition,the emotional attribute ex-pressed by public opinion text is supplemented by BERT.In order to make better use of the public opinion text representation,this paper designs a deep prediction model based on attention mechanism and LSTM.The public opinion text representation obtained by the public opinion node algorithm and the processed historical transaction information are input into the constructed deep learning model for stock trend prediction.The experimental results on Twitter dataset and Yahoo financial discussion board dataset show that the deep prediction model based on social platform public opinion and historical price information proposed in this paper can surpass the common prediction models and achieve better effect on short-term stock price trend prediction.In addition,based on the algorithm research,this paper provides a visual interactive stock market trend prediction platform,which integrates the algorithm proposed above.The platform is convenient for users to view the target stock and predict the short-term stock price,and provides advice for users' investment behavior.