Investor sentiment about future returns of financial instruments is a highly relevant information source for investment managers and other stakeholders in the financial industry. Investor sentiments are abundant in financial blog texts. Making use of these sentiments constitutes a massive information management challenge when considering the millions of blog articles with everchanging and growing amounts of information that need to be acquired and interpreted. We propose a novel approach for investor sentiment extraction from blogs by combining machine-learning on the document-level and knowledgebased information extraction on the sentence-level. The proposed artifact is a financial instrument-specific investor sentiment extraction method, which we apply to a set of blog articles. The evaluation suggests that the combined approach achieves a higher precision compared to a standalone knowledge-based approach.
Klein, Achim; Altuntas, Olena; Riekert, Martin; and Dinev, Velizar, "A Combined Approach for Extracting Financial Instrument-Specific Investor Sentiment from Weblogs" (2013). Wirtschaftsinformatik Proceedings 2013. 44.