Analysis to Online Stock for Decision Approach of Investor

The Internet service provides the wider opportunity for the investors to post online opinions that they share with fellow investors. Attitude analysis of online opinion posts can be facilitating both investors investment on decision making and stock companies risk perception. In this paper develops novel sentiment ontology to conduct context-sensitive sentiment analysis of stock markets are opinion post on online. A typical financial has been selected as an experimental platform of financial review data was collected. Computational results show that the statistical machine learning approach has higher classification perfection than the semantic approach. Then results also imply that investor sentiment has a particularly strong effect for rate of stocks relative to growth stocks. It has been reported that these message boards can have a significant impact reflect on the financial markets.