| dc.contributor.advisor | 陳威光<br>李桐豪 | zh_TW |
| dc.contributor.author (Authors) | 黃于珊 | zh_TW |
| dc.contributor.author (Authors) | Huang, Yu Shan | en_US |
| dc.creator (作者) | 黃于珊 | zh_TW |
| dc.creator (作者) | Huang, Yu Shan | en_US |
| dc.date (日期) | 2017 | en_US |
| dc.date.accessioned | 11-Jul-2017 11:31:01 (UTC+8) | - |
| dc.date.available | 11-Jul-2017 11:31:01 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Jul-2017 11:31:01 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0104352027 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/110803 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 104352027 | zh_TW |
| dc.description.abstract (摘要) | 本研究以1993年到2017年3月間的193篇FOMC Minutes作為研究素材，先採監督式學習方法，利用潛在語意分析（latent semantic analysis，LSA）萃取出升息、降息及不變樣本的潛在語意，再以線性判別分析(Linear Discriminant Analysis, LDA)進行分類；此外，本研究亦透過非監督式學習方法中的探索性資料分析(Exploratory Data Analysis, EDA)，試圖從FOMC Minutes中找尋相關變數。研究結果發現，LSA可大致區分出升息、降息及不變樣本的特徵，而EDA能找出不同時期或不同類別下的重要單詞，呈現文本的結構變化，亦能進行文本分群。 | zh_TW |
| dc.description.abstract (摘要) | In this study, 193 FOMC Minutes from 1993 to March 2017 were used as research materials. The latent semantic analysis (LSA) in supervised learning methods was used to extract the potential semantics of interest rate increased, decreased, and unchanged samples, and then linear discriminant analysis (LDA) was used for classification. In addition, this study attempts to find relevant variables from FOMC Minutes through exploratory data analysis (EDA) in unsupervised learning methods. The results show that LSA can distinguish the characteristics of interest rate increased, decreased, and unchanged samples. EDA can find relevant words in different periods or different categories, show changes in the text structure, and can also classify the texts. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1第一節 研究動機 1第二節 研究目的 1第二章 文獻回顧 4第一節 美國聯邦儲備系統及 FOMC Minutes 4第二節 潛在語意分析 6第三節 文字探勘的前置流程 8第四節 信息論 8第三章 研究方法 12第一節 資料介紹 12第二節 潛在語意分析與文本分類 13第三節 探索性資料分析 14第四章 研究結果 15第一節 潛在語意分析與文本分類 15第二節 探索性資料分析 20第五章 結論與建議 27參考文獻 29附錄 31 | zh_TW |
| dc.format.extent | 1224246 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0104352027 | en_US |
| dc.subject (關鍵詞) | 聯準會 | zh_TW |
| dc.subject (關鍵詞) | 利率決議 | zh_TW |
| dc.subject (關鍵詞) | 文字探勘 | zh_TW |
| dc.subject (關鍵詞) | 潛在語意分析 | zh_TW |
| dc.subject (關鍵詞) | 探索性資料分析 | zh_TW |
| dc.subject (關鍵詞) | Fed | en_US |
| dc.subject (關鍵詞) | FOMC minutes | en_US |
| dc.subject (關鍵詞) | Text mining | en_US |
| dc.subject (關鍵詞) | LSA | en_US |
| dc.subject (關鍵詞) | EDA | en_US |
| dc.title (題名) | 文字探勘在總體經濟上之應用－ 以美國聯準會會議紀錄為例 | zh_TW |
| dc.title (題名) | The application of text mining on macroeconomics : a case study of FOMC minutes | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 一、中文文獻1.吳軍(2016)。數學之美。人民郵電出版社。2.吳今朝 譯(2016)。基於R語言的自動數據收集。機械工業出版社。3.王建興，從搜尋引擎到文字探勘，檢自：http://www.ithome.com.tw/voice/903614.黄耀鹏，R文本挖掘之tm包，檢自： http://yphuang.github.io/blog/2016/03/04/text-mining-tm-package/二、英文文獻1.Carlo Rosa, (2013). The Financial Market Effect of FOMC Minutes, Economic Policy Review, Volume 19, Number 2.2.Claude Elwood Shannon, (1948). A Mathematical Theory of Communication, The Bell System Technical Journal, Vol. 27, 379–423, 623–656.3.Deborah J. Danker and Matthew M. Luecke, (2005). Background on FOMC Meeting Minutes, Federal Reserve Bulletin, issue Spr, 175-179.4.Ellyn Boukus and Joshua V. Rosenberg, (2006). The Information Content of FOMC Minutes, Federal Reserve Bank of New York.5.Ingo Feinerer, Kurt Hornik, and David Meyer, (2008). Text Mining Infrastructure in R, Journal of Statistical Software, Vol 25 (2008) ,Issue 5.6.Jack C. Yue and Murray K. Clayton, (2005). A Similarity Measure based on Species Proportions, Communications in Statistics - Theory and Methods, Volume 34.7.Martin F. Porter, (1980). An algorithm for suffix stripping, Program 14 (3): 130-137.8.S.Kannan and Vairaprakash Gurusamy, (2014). Preprocessing Techniques for Text Mining - An Overview, International Journal of Computer Science & Communication Networks, Vol 5(1),7-16.9.Tim Loughran and Bill Mcdonald, (2016).Textual Analysis in Accounting and Finance:A Survey. Journal of Accounting Research, Volume 54, Issue 4.10.Zhichao Han, (2012). Data and Text Mining of Financial Markets using News and Social Media, University of Manchester. | zh_TW |
