Application of social media sentiment analysis to finance domain stock market trending study - Liu, Yizhou (2018)
The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied in this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced strategies have been used to improve the performance of the learning-based predictors. However, performing successful stock market prediction is still a challenge. This report aims to successfully predict stock price through analyzing the relationship between the stock price and the news sentiments. A novel enhanced learning-based method for stock price prediction is proposed that considers the effect of news sentiments. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using the real stock price data set with an improvement of performance to reduce the Mean Square Error (MSE). The research work and findings of this report not only demonstrate the merits of the proposed method, but also points out the correct direction for future work in this area.