政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/110803
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113392/144379 (79%)
造访人次 : 51193469      在线人数 : 929
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/110803


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/110803


    题名: 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
    The application of text mining on macroeconomics : a case study of FOMC minutes
    作者: 黃于珊
    Huang, Yu Shan
    贡献者: 陳威光
    李桐豪
    黃于珊
    Huang, Yu Shan
    关键词: 聯準會
    利率決議
    文字探勘
    潛在語意分析
    探索性資料分析
    Fed
    FOMC minutes
    Text mining
    LSA
    EDA
    日期: 2017
    上传时间: 2017-07-11 11:31:01 (UTC+8)
    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。
    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.
    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/90361
    4.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.
    描述: 碩士
    國立政治大學
    金融學系
    104352027
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0104352027
    数据类型: thesis
    显示于类别:[金融學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    202701.pdf1195KbAdobe PDF2721检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈