  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, 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.
