Volume 2 - Issue 3
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 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.
Paper Details
PaperID: 6702641
Author Name: S. Saradha and G. Magesh
Author Email: -
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Country: -
Keywords: Sentiment, Stock Markets, Ontology, Financial and Accuracy
Volume: Volume 2
Issues: Issue 3
Issue Type: Issue
Year: 2015
Month: September
Pages:167-169