人是組織中的重要資產，每一位醫院員工有其所負責的專業職責，同時也直接影響患者對組織的印象，可見員工是醫院不可或缺的角色，也使得組織中的人力資源管理變得極為重要。因此，組織在實行人力資源管理流程時，應從源頭細心把關，而瞭解與組織適配的人力資源特徵，與甄選聘用合適的員工將是首要關鍵。
　　本研究將建置一個人格特質屬性之留任預測模型，藉由履歷自傳的文字資料，運用LDA主題模型方法萃取主題特徵，並基於MBTI理論來自動化判斷人格特質，同時將比對組織的價值觀，協助組織找出較適配的人格特質，而後再透過機器學習分類方法建置留任分類模型，以預測應徵者自主留任行為模式。
　　根據實驗結果，本研究不僅找出與組織較適配的主要人格特質屬性，也驗證員工若與組織間契合程度越高，越可能展現正向行為。而透過本研究之成果可協助組織提早瞭解員工自主留任可能的狀況，以利組織能適時展開人員留任管理相關方案，降低員工自主離職與造成組織成本損失的情況。

Human resource is important asset in an organization. Every hospital employee is responsible for their professional responsibilities, and it also directly affects the patient's impression of the organization. We can find that the employee is an indispensable role of the hospital, and how important is the human resource management in the organization. Therefore, the organization should carefully implement the human resources management process, understanding the human resource characteristics which is adapted to the organization and selecting the right employees.
This study proposes a retention prediction model of personality trait attributes. We adopt LDA topic model method to extract topic features by the autobiographical texts, and base on MBTI theory to automatically judge personality traits. At the same time, we compare the values of the organization to help the organization to find a more appropriate personality trait. Finally, the retention classification model is built through the machine learning method to predict the applicant's autonomous retention behavior pattern.
According to the experimental results, this study not only finds the main personality trait attributes that are more suitable for the organization, but also verifies that the higher degree of person-organization fit, the more likely the employees will show positive behavior. The results of this research can help the organization to understand the status of employees' retention, and carry out the retention management plan in a timely manner to reduce the voluntary turnover of employees and the loss of organizational costs.

Agarwal, B. (2014). Personality detection from text: A Review. International Journal of Computer System, 1(1), 1-4.

Alam, F., Stepanov, E. A., & Riccardi, G. (2013). Personality traits recognition on social network-facebook. Paper presented at the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, Massachusetts.

Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919.

Allport, G. W. (1961). Pattern and growth in personality. New York: Holt, Rinehart and Winston.

Berkman, E. T., & Reise, S. P. (2011). A conceptual guide to statistics using SPSS. California: SAGE Publications.

Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357-365.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4-5), 993-1022.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Cattell, R. B. (1950). Personality: A systematic theoretical and factual study. New York: McGraw-Hill.

Chatman, J. A. (1989). Improving Interactional Organizational Research - A Model Of Person-Organization Fit. Academy of Management Review, 14(3), 333-349.

Corr, P. J., & Matthews, G. (2009). The Cambridge Handbook of Personality Psychology. New York: Cambridge University Press Cambridge.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Darley, J. M., Glucksberg, S., & Kinchla, R. A. (1991). Psychology. New Jersey: Prentice-Hall.

Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.

Faliagka, E., Tsakalidis, A., & Tzimas, G. (2012). An integrated e-recruitment system for automated personality mining and applicant ranking. Internet Research, 22(5), 551-568.

Glasser, W. (1965). Reality therapy : a new approach to psychiatry. New York: Harper & Row.

Grady, J. (2006). A simple statement: A guide to nonprofit arts management and leadership. Portsmouth: Heinemann Drama.

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl 1), 5228-5235.

Hernández, Y., Peña, C. A., & Martínez, A. (2018). Model for Personality Detection Based on Text Analysis. Paper presented at the Mexican International Conference on Artificial Intelligence, Guadalajara, Mexico.

Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1-2), 177-196.

Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments. Odessa: Psychological Assessment Resources.

Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. Retrieved from Taiwan, Taipei: Department of Computer Science and Information Engineering, University of National Taiwan

Huselid, M. A. (1995). The impact of human-resource management-practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38(3), 635-672.

Imel, Z. E., Steyvers, M., & Atkins, D. C. (2015). Computational Psychotherapy Research: Scaling up the Evaluation of Patient-Provider Interactions. Psychotherapy, 52(1), 19–30.

Jung, C. G. (1923). Psychological types: or the psychology of individuation. Oxford: Harcourt Brace.

Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihok, G., & Den Hartog, D. N. (2018). Text Mining in Organizational Research. Organizational Research Methods, 21(3), 733-765.

Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals' fit at work: A meta-analysis of person-job, person-organization, person-group, and person-supervisor fit. Personnel Psychology, 58(2), 281-342.

Kwantes, P. J., Derbentseva, N., Lam, Q., Vartanian, O., & Marmurek, H. H. C. (2016). Assessing the Big Five personality traits with latent semantic analysis. Personality and Individual Differences, 102, 229-233.

Lee, J., & Hong, Y. S. (2013). Business model mining: Analyzing a firm’s business model with text mining of annual report. Paper presented at the 19th International Conference on Engineering Design (ICED13), Seoul, Korea.

Liu, Y. Z., Wang, J. J., & Jiang, Y. C. (2016). PT-LDA: A latent variable model to predict personality traits of social network users. Neurocomputing, 210, 155-163.

Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. Paper presented at the Proceedings of the conference on empirical methods in natural language processing, Edinburgh, Scotland, UK.

Myers, I. B., McCaulley, M. H., Quenk, N. L., & Hammer, A. L. (1998). MBTI manual: A guide to the development and use of the Myers-Briggs Type Indicator (Vol. 3). Palo Alto: Consulting Psychologists Press.

Robbins, S. P. (2001). Organizational Behavior. New Jersey: Prentice-Hall.

Schneider, B., Goldstein, H. W., & Smith, D. B. (1995). The ASA framework: An update. Personnel Psychology, 48(4), 747-773.
Tirunillai, S., & Tellis, G. J. (2014). Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation. Journal of Marketing Research, 51(4), 463-479.

Zikmund, W. G. (2000). Business research methods. London, UK: South-Western College.

陳憲煜. (2013). 以商業智慧方法論分析醫療專業人員職涯異動與醫事機構經營績效之研究. 國立成功大學高階管理碩士在職專班碩士論文, Available from 台灣博碩士論文知識加值系統。

衛生福利部醫事司. (2017). 醫院評鑑基準(醫學中心適用)【公告】. 衛生福利部. Retrieved from https://dep.mohw.gov.tw/DOMA/cp-948-33633-106.html