Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, the authors built tagging models based on the Conditional Random Field (CRF) techniques, considering a variety of combinations of linguistic factors including morphology, orthography, predicate-argument structure, syntax, and simple semantics. Their results show that the CRF models are reasonably effective to find opinion holders in experiments when they adopted the popular MPQA corpus for training and testing. The contribution of their paper is to identify opinion patterns in MultiWord Expressions (MWEs) forms rather than in single word forms.