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題名 Financial Sentiment Analysis for Risk Prediction
作者 Wang, Chuan-Ju;Tsai, Ming-Feng;Liu, Tse;Chang, Chin-Ting
蔡銘峰
貢獻者 資科系
日期 2013-10
上傳時間 22-Jun-2016 17:10:38 (UTC+8)
摘要 This paper attempts to identify the importance of sentiment words in financial reports on financial risk. By using a financespecific sentiment lexicon, we apply regression and ranking techniques to analyze the relations between sentiment words and financial risk. The experimental results show that, based on the bag-of-words model, models trained on sentiment words only result in comparable performance to those on origin texts, which confirms the importance of financial sentiment words on risk prediction. Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of companies. As a result, these findings are of great value for providing us more insight and understanding into the impact of financial sentiment words in financial reports.
關聯 Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP `13), 802-808, 2013
資料類型 conference
dc.contributor 資科系
dc.creator (作者) Wang, Chuan-Ju;Tsai, Ming-Feng;Liu, Tse;Chang, Chin-Ting
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2013-10
dc.date.accessioned 22-Jun-2016 17:10:38 (UTC+8)-
dc.date.available 22-Jun-2016 17:10:38 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2016 17:10:38 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98232-
dc.description.abstract (摘要) This paper attempts to identify the importance of sentiment words in financial reports on financial risk. By using a financespecific sentiment lexicon, we apply regression and ranking techniques to analyze the relations between sentiment words and financial risk. The experimental results show that, based on the bag-of-words model, models trained on sentiment words only result in comparable performance to those on origin texts, which confirms the importance of financial sentiment words on risk prediction. Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of companies. As a result, these findings are of great value for providing us more insight and understanding into the impact of financial sentiment words in financial reports.
dc.format.extent 800435 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP `13), 802-808, 2013
dc.title (題名) Financial Sentiment Analysis for Risk Prediction
dc.type (資料類型) conference