A Decision Support Approach for Online Stock Forum Sentiment Analysis
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 novel sentiment ontology to conduct context-sensitive sentiment analysis of online opinion posts in stock markets. 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 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. It has been reported that these message boards can have a significant impact on financial markets.
Author Name: S. Saradha and G. Magesh
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Keywords: Sentiment, Stock Markets, Ontology, Financial and Accuracy