Sentimental Analysis of Chinese New Social Media for stock market information

Abstract

The popularity of social media provides a new platform to collect big social data. With the development of social sentiment analysis, high business value extracted from social data are applied to various fields. Asset price prediction, as an emerging topic based on the behavioral economics, is closely linked to social data analysis. This research aims to explore the effort of sentiment analysis data in the prediction of China composite index. Data from Sina Weibo and financial community is processed to get the useful sentiment information. A linear regression model and a multilayer neural network algorithm are used to prove the relationship between social data and price market prediction. The experiments show a strong relationship between the numbers of negative sentiment and a multilayer perceptron model is effectively built to predict the composite index.