| dc.contributor | 財管系 | en_US |
| dc.creator (作者) | 張元晨 | zh_TW |
| dc.creator (作者) | Chen, Chien-Liang ; Liu, Chao-Lin ; Chang, Yuan-Chen ; Tsai, Hsiangping | en_US |
| dc.date (日期) | 2013.03 | en_US |
| dc.date.accessioned | 26-May-2014 10:28:08 (UTC+8) | - |
| dc.date.available | 26-May-2014 10:28:08 (UTC+8) | - |
| dc.date.issued (上傳時間) | 26-May-2014 10:28:08 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/66213 | - |
| dc.description.abstract (摘要) | Financial statements contain quantitative information and manager’s subjective evalua-tion of firm’s financial status. Using information released in U.S. 10-K filings. Both qualita-tive 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 ran-dom field (CRF) techniques, considering a variety of combinations of linguistic factors in-cluding morphology, orthography, predicate-argument structure, syntax, and simple seman-tics. 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 ob-fuscate 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. | en_US |
| dc.format.extent | 849847 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journey of information Science and Engineering, Vol.29, No.3, pp.25-50 | en_US |
| dc.subject (關鍵詞) | financial text mining, opinion mining, sentiment analysis, financial, multiword expressions, natural language processing, MPQA, information extraction | en_US |
| dc.title (題名) | Opinion mining for relating multiword subjective expressions and annual earnings in US financial statements | en_US |
| dc.type (資料類型) | article | en |
