A Decision Support Approach for Online Stock Forum Sentiment Analysis

Abstract

The Internet provides the opportunity for investors to post online opinions that they share with fellow investors. Sentiment analysis of online opinion posts can facilitate both investors' investment decision making and stock companies' risk perception. This paper develops a novel sentiment ontology to conduct context-sensitive sentiment analysis of online opinion posts in stock markets. The methodology integrates popular sentiment analysis into machine learning approaches based on support vector machine and generalized autoregressive conditional heteroskedasticity modeling. A typical financial website called Sina Finance has been selected as an experimental platform where a corpus of financial review data was collected. Empirical results suggest solid correlations between stock price volatility trends and stock forum sentiment. Computational results show that the statistical machine learning approach has a higher classification accuracy than that of the semantic approach. Results also imply that investor sentiment has a particularly strong effect for value stocks relative to growth stocks.