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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46471完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | Jeng-Chien Chen | en |
| dc.contributor.author | 陳正乾 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:10:45Z | - |
| dc.date.available | 2010-07-28 | |
| dc.date.copyright | 2010-07-28 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-23 | |
| dc.identifier.citation | 1. E. Agichtein, E. Brill , S. Dumais, “Improving Web Search Ranking by Incorporating User Behavior Information,” Annual ACM Conference on Research and Development in Information Retrieval, SESSION: User behavior and modeling, pp. 19 – 26, 2006.
2. N. Askitas and K. F. Zimmerman, “Google econometrics and unemployment forecasting,” Applied Economics Quarterly,” vol.55, pp. 107-120, 2009. 3. R. Baeza-Yates, A. Tiberi, “Extracting semantic relations from query logs,” International Conference on Knowledge Discovery and Data Mining, pp. 76 – 85, 2007. 4. C. Birchenhall, H. Jessen, D. Osborn, P. Simpson, “Predicting US’ business-cycle regimes,” Journal of Business and Economic Statistics, vol. 17, pp. 313-323, 1999. 5. A. F. Burns and W. C. Mitchell, “Measuring Business Cycles,” New York: National Bureau of Economic Reseach, 1946. 6. M. Camacho, P. Q. Gabriel, “This is what the leading indicators lead,” Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1) pp. 61-80, 2002. 7. H. Choi and H. Varian, “Predicting the present with google trends,” Google Technical Report, April 2009. 8. F. D’Amuri, “Predicting unemployment in short samples with internet job search query data,” unpublished. 9. G. Eysenbach, “Infodemiology: The epidemiology of (mis)information,” Am. J. Med., vol. 113, pp. 763-765, 2002. 10. G. Eysenbach, ”Infodemiology: tracking flu-related searches on the web for syndromic surveillance,” AMIA Annu Symp Proc, pp. 244-248, 2006. 11. Z. H. Fang, J. S. Tzeng, C. C. Chen and T. C. Chou, “A Study of Machine Learning Models in Epidemic Surveillance: Using the Query Logs of Search Engines,” 14th Pacific Asia Conference on Information Systems, in process. 12. J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, “Detecting influenza epidemics using search engine query data. Nature,” Nature, vol. 457, pp. 1012-1014, 2009. 13. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in C. S. Mellish, (ed.), Proceedings of IJCAI-95 , pp. 1137-1143. Morgan Kaufmann., 1995. 14. R. Kumar and A. Tomkins, ”A characterization of online search behavior,” IEEE Data Eng. Bull., 32(2):3--11, 2009. 15. K. P. Lin and P. F. Pai, “A fuzzy support vector regression model for business cycle predictions,” Expert Systems with Applications, vol. 37, issue 7, pp. 5430-5435 , July 2010. 16. C. D. Manning and H. Schutze, “Foundations of Statistical Natural Language Processing,” MIT Press, ISBN 0-262-13360-1, 2003. 17. C. D. Manning and P. Raghavan and H. Schutze, “Introduction to information retrieval,” Cambridge University Press, ISBN: 0521865719, 2008. 18. T. Schmidt and S. Vosen, “Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends,” Ruhr Economic Paper No. 155, November 2009. 19. P. Turney, “Mining the web for synonyms: PMI-IR versus LSA on TOEFL,” in Proceedings of the Twelfth European Conference on Machine Learning, 2001. 20. Birchenhall C, Jessen H, Osborn D, Simpson P. 1999, “Predicting US’ business-cycle regimes,” Journal of Business and Economic Statistics, vol 17, Pages 313-323 21. Ernst A. Boehm, Peter M. Summers, “Analysing and Forecasting Business Cycles with the Aid of Economic Indicators,' International Journal of Management Reviews, vol 1, issue 3, pp 245-277, 2003. 22. Charles C. Holt, 2004, “Forecasting seasonals and trends by exponentially weighted moving averages,” International Journal of Forecasting, vol. 20, Issue 1, January-March 2004, Pages 5-10. 23. Y. Yang and J. Pedersen, “A comparative study on feature selection in text categorization,” In International Conference on Machine Learning (ICML), 1997. 24. J. M. Lucas and M. S. Saccucci, ”Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements,” Technometrics, vol. 32, No. 1 pp. 1-12, 1990. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46471 | - |
| dc.description.abstract | 景氣循環以及未來經濟趨勢一直以來都是各國政府以及學者所關心的議題,透過景氣循環的分析,可以了解一個國家目前的經濟狀況以及未來的經濟發展趨勢。國家的經濟發展與景氣循環息息相關,許多的研究都致力於景氣循環的分析以及預測,景氣的現況對於政府以及投資人是相當重要的資訊。傳統上,許多用來觀測景氣的指標都會使用經濟變數來編制,由於這些經濟變數來自於不同的政府部門,往往需要許多資料收集以及資料統計的時間,這些時間會造成指標發佈的延遲,對政府與投資人而言,這些指標發佈的延遲會增加決策上的不確定性。為了要解決這個問題,我們想要使用搜尋記錄(Query Log) 來作為景氣的指標。搜尋記錄具有即時性,幾乎不需要任何的資料統計以及資料蒐集的時間,如此便可以減少景氣指標資訊發布的延遲,進而減少決策上的不確定性。在過去,網路公司並不提供搜尋記錄的資料,一般人無法得到搜尋記錄的資料。在2008年,Google推出服務 - Google Insights for Search,讓一般的使用者也可以使用搜尋記錄的資料。透過這些資料,我們觀察到使用者的確在不同的狀況下會有不同的搜尋行為。我們試著找出搜尋趨勢跟景氣循環走勢具有相關性的搜尋關鍵字。並且利用這些關鍵字的搜尋記錄來當作景氣指標。我們將提出尋找可以當作景氣指標的關鍵字的方法,並且建立一個簡單的模型,驗證這些關鍵字辨認目前景氣狀況以及預測未來景氣狀況的能力。 | zh_TW |
| dc.description.abstract | Identifying status of business cycles is critical to governments and enterprises when building business strategies. Traditionally, economic variables, such as industrial production, stock price index, manufacturing sales, are selected to compose business cycle indicators, which altogether evaluate business cycle status. In general, the release of economic variables involves long data processes that delay the announcement of business status. The announced business status thus is not timely and could increase the uncertainty of business decision making. In this work, we employ query logs of search engines for business cycle identification. As query logs are readily available through online Web services, they can provide timely and accurate information about business status. We propose a feature selection method to identify query terms appropriate for business cycle identification. Evaluation results show that the identified query terms are effective indicators and the proposed method models business cycles correctly. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:10:45Z (GMT). No. of bitstreams: 1 ntu-99-R97725017-1.pdf: 1933274 bytes, checksum: e3c97950cf16b29b8a20007e5555ea3a (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 論文摘要 i
THESIS ABCSTRACT ii Table of Contents iii List of Figures v List of Tables vi Chapter 1. Introduction 1 Chapter 2. Related Work 6 2.1 Applications of Query Logs 6 2.2 Business Indicators 8 2.2.1. Business Indicators in Taiwan 8 2.2.2. Business Indicators in Japan 11 2.2.3. Business Indicators in the U.S. 14 2.2.4. Discussion 16 Chapter 3. Methodology 17 3.1 System Architecture 17 3.2 Feature Selection Methods 18 3.2.1. Frequency Based Approach 18 3.2.2. Pointwise Mutual Information 19 3.2.3. Modified PMI-IR 20 3.2.4. Correlation Coefficient 21 3.3 Classification Model Construction 23 3.3.1. Naive Bayes Classifier 23 Chapter 4. Experiments 25 4.1 Dataset 25 4.2 Feature Sets 27 4.3 Classification comparison 29 4.4 Examples of Query Term based Business Status Indicators 33 Chapter 5. Conclusion and Future Work 39 Reference 41 | |
| dc.language.iso | en | |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 景氣指標 | zh_TW |
| dc.subject | 企業智慧 | zh_TW |
| dc.subject | text mining | en |
| dc.subject | business indicator | en |
| dc.subject | business intelligence | en |
| dc.title | 以搜尋紀錄為基礎之景氣指標探討 | zh_TW |
| dc.title | Identification of significant business cycle indicators using query logs of search engine | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Meng Chang Chen),陳銘憲(Ming-Syan Chen) | |
| dc.subject.keyword | 文字探勘,景氣指標,企業智慧, | zh_TW |
| dc.subject.keyword | text mining,business indicator,business intelligence, | en |
| dc.relation.page | 43 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-07-26 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位: | 資訊管理學系 | |
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