Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/110803
|
| Title: | 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 |
| Authors: | 黃于珊
Huang, Yu Shan |
| Contributors: | 陳威光
李桐豪
黃于珊
Huang, Yu Shan |
| Keywords: | 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 |
| Date: | 2017 |
| Issue Date: | 2017-07-11 11:31:01 (UTC+8) |
| Abstract: | 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. |
| Reference: | 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. |
| Description: | 碩士
國立政治大學
金融學系
104352027 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104352027 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| 202701.pdf | 1195Kb | Adobe PDF2 | 720 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
