臺灣博碩士論文加值系統

本研究使用文字探勘技術分析交大高階經理人培訓班報名資料，探討學員們期望從課程中學到的管理知識能否以Katz的管理知能模型進行分類。在進行文字探勘處理時，由於中文斷詞結果未能符合本研究資料的精神，如「經營管理」、「經營企業」無法被準確的斷詞。因此，使用N-gram與TFIDF自建關鍵詞彙。研究結果發現，由於資料收集的目的是以能在培訓班學到的內容為主，而非針對學員的整體管理知能需求進行調查，所以在前100個關鍵詞彙中仍有17%的詞彙無法以Katz的管理知能模型進行分類。在符合Katz管理知能模型關鍵詞彙中，有12%屬於技術能力(Technical skills)、17%屬於人際能力(Human skills)、71%屬於概念化能力(Conceptual skills)，符合在高階經理人培訓班中預期學到的管理知能。本研究建立的關鍵詞彙(1)可做為培訓班學員管理知能核心需求之基礎，用於規劃課程內容與選擇講師。(2)可做為課程問卷的管理知能需求選項，針對選項設計適合不同管理層級與學員背景的課程。(3)可建立屬於交大高階經理人培訓班的專屬詞典，做為日後進行文字探勘的中文斷詞詞典，並進一步發展成為管理知能需求的「領域詞典」。(4)受限於分析方法，未進行詞彙前後文判斷，造成詞彙意義被簡化解讀。本研究單純以詞彙的TFIDF進行關鍵詞彙的選擇。

This study uses text mining to analyze the application materials of the NCTU senior manager training courses and discusses whether the management knowledge that the trainees expect to learn from the course can be classified by Katz's management model. In the process of text mining, because the results of Chinese word segmentation cannot fulfill the spirit of the study, such as "business management" and "business enterprise" cannot be precisely segmented. Therefore, to rebuild the keywords by using N-gram and TFIDF. The results of the study found that since the purpose of data collection was to focus on the study content that could be learned in the training courses, rather than the overall management knowledge needs by the trainees. Thus, there still have had 17% of the vocabulary in the top 100 keywords cannot be classified by Katz's management model. In those keywords meet Katz's management model, 12% were Technical skills, 17% related to Human skills, and 71% belonged to Conceptual skills. They were in line with the expectations of the trainees. The keywords established in this study:(1) They can be used as the training core requirements base for management knowledge, to plan course content and select lecturers. (2) As the optional function used in the course questionnaire to design specific curriculum for different management levels and background of students. (3) To establish a NCTU senior manager training course “dedicated dictionary” that can apply in Chinese word segmentation for text mining in the future. And further developed into a "domain dictionary" for managing knowledge needs. (4) Limited by the analysis method, the meaning of the vocabulary to be expressed is simplified. This study simply selects the key words with the TFIDF weights of vocabulary.

中文摘要 I
英文摘要 II
目錄 III
表目錄 IV
圖目錄 V
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 研究流程 2
第二章 文獻探討 3
2.1 Katz管理知能模型 3
2.2 文字探勘 5
2.3 N-gram 7
2.4 TFIDF 7
第三章 研究資料預處理 9
3.1 資料介紹 9
3.2 資料預處理 10
3.3 小結 11
第四章 研究方法 12
4.1 執行流程 12
4.2 中文斷詞工具測試 13
4.3 自行建立關鍵詞彙 16
第五章 研究結果 20
5.1 前100大關鍵詞彙 21
5.2 以Katz管理知能模型進行關鍵詞彙分類 23
5.3 無法以Katz管理知能模型分類的詞彙 27
5.4管理階層關鍵詞彙分析 28
5.5小結 39
第六章 結論與建議 40
6.1 研究結果與討論 40
6.2 實務建議與研究限制 42
參考文獻 45
附錄一：交大高階經理人培訓班線上班名表 47
附錄二：資料預處理程式（EXCEL VBA） 49
附錄三：scikit-learn 52
附錄四：Python程式 53

中文
1. 毛治國(2018).管理.新竹市.交大出版社.
2. 林麗芬(1987).科技主管的管理知能需求分析.國立交通大學管理科學研究所碩士論文.
3. 周濟群, 戚玉樑, & 曾建勛(2012). 以詞彙表為基礎的知識本體雛型建構研究─ 以 [公司治理] 領域知識為例. 圖書資訊學研究, 6(2), 37-81.
4. 葉乃菁. (2009). 本體論與文字探勘在注意力經濟下之應用研究與價值. 科技發展政策報導, (4), 93-100.

二、英文
1. Abdous, M. H., & He, W. (2011). Using text mining to uncover students' technology‐related problems in live video streaming. British Journal of Educational Technology, 42(1), 40-49.
2. Aizawa, A. (2003). An information-theoretic perspective of tf–idf measures. Information Processing & Management, 39(1), 45-65.
3. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
4. Mete, M., Yuruk, N., Xu, X., & Berleant, D. (2009). Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach. In Data Engineering (pp. 225-243). Springer, Boston, MA.
5. Hotho, A., Nürnberger, A., & Paaß, G. (2005, May). A brief survey of text mining. In Ldv Forum (Vol. 20, No. 1, pp. 19-62).
6. Jackson, K. M., & Trochim, W. M. (2002). Concept mapping as an alternative approach for the analysis of open-ended survey responses. Organizational Research Methods, 5(4), 307-336.
7. Joachims, T. (1996). A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization (No. CMU-CS-96-118). Carnegie-mellon univ pittsburgh pa dept of computer science.
8. Katz, R, L.(1974). Skills of an Effective Administrator. Harvard Business Review ,(52:5), 90-102.
9. Kešelj, V., Peng, F., Cercone, N., & Thomas, C. (2003, August). N-gram-based author profiles for authorship attribution. In Proceedings of the conference pacific association for computational linguistics, PACLING (Vol. 3, pp. 255-264).
10. Kurgan, L. A., & Musilek, P. (2006). A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review, 21(1), 1-24.
11. Losiewicz, P., Oard, D. W., & Kostoff, R. N. (2000). Textual data mining to support science and technology management. Journal of Intelligent Information Systems, 15(2), 99-119.
12. Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 311-318). Association for Computational Linguistics.
13. Peterson, T. O., & Van Fleet, D. D. (2004). The ongoing legacy of RL Katz: An updated typology of management skills. Management decision, 42(10), 1297-1308.
14. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
15. Tang, C., & Guo, L. (2015). Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication. Marketing Letters, 26(1), 67-80.
16. Tseng, F. S., & Chou, A. Y. (2006). The concept of document warehousing for multi-dimensional modeling of textual-based business intelligence. Decision Support Systems, 42(2), 727-744.
17. Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60-76.
18. Cavnar, W. B., & Trenkle, J. M. (1994). N-gram-based text categorization. Ann arbor mi, 48113(2), 161-175.

三、網站
1. 中研院資訊科學研究所，中文斷詞系統，線上展示斷詞服務，最後存取日期2018/5/13，網站：http://ckipsvr.iis.sinica.edu.tw/.