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Journal of Information Science and Engineering, Vol. 29 No. 4, pp. 743-764 (July 2013)


Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements


CHIEN-LIANG CHEN1, CHAO-LIN LIU2, YUAN-CHEN CHANG3 AND HSIANG-PING TSAI4
1,2Department of Computer Science
3Department of Finance
1,2,3National Chengchi University
Taipei, 116 Taiwan
4College of Management
Yuan-Ze University,
Chungli, 320 Taiwan
E-mail: {198753013; 2chaolin; 3yccchang}@nccu.edu.tw, 4hptsai@saturn.yzu.edu.tw

Financial statements contain quantitative information and manageríŽs subjective evaluation of firmíŽs 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, we 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. Our results show that the CRF models are reasonably effective to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. The contribution of our paper is to identify opinion patterns in multiword expressions (MWEs) forms rather than in single word forms. We find that the managers of corporations attempt to use more optimistic words to obfuscate negative financial performance and to accentuate the positive financial performance. Our results also show that decreasing earnings were often accompanied by ambiguous and mild statements in the reporting year and that increasing earnings were stated in assertive and positive way.

Keywords: financial text mining, opinion mining, sentiment analysis, financial multiword expressions, natural language processing, MPQA, information extraction

Full Text (ą■Ąň└╔) Retrieve PDF document (201307_09.pdf)

Received February 17, 2012; accepted June 20, 2012.
Communicated by Toyoaki Nishida.