Stock price prediction for Google based on LSTM model with sentiment analysis

Abstract

Data analytics is increasingly widely used in economic and financial fields, with one of the more important applications being the prediction of stock price changes. However, the prediction of stock price changes is challenging because stock price changes are often uncertain and affected by multiple factors. This study is designed to use the LSTM model to predict stock price changes, and in the construction of the model to consider the psychological and emotional changes of investors, adding a sentiment analysis, combined with the sentiment index obtained from the sentiment analysis and the original stock price data as the input data for the prediction model. During the experiment, a comparison experiment was set up, i.e., only using the basic LSTM prediction model to predict stock price changes and the improved LSTM prediction model with the sentiment index obtained from the added sentiment analysis to predict stock price changes. After the comparison, the prediction results obtained by the LSTM model with the addition of sentiment analysis are more accurate, which on the one hand indicates that the change of investors' psychological sentiment will have an impact on the stock price change, and indicates that the prediction results obtained by the prediction model that considers the change of investors' sentiment are more accurate. The improved LSTM prediction model can help investors to effectively avoid possible risks when investing in stocks and thus gain more profit.