近年受到人口高齡化的影響及國人消費意識的提升，我國醫療市場競爭激烈，使醫療產業走向「以人為本」的服務導向。提升醫療服務品質及提高顧客滿意度成為醫療產業的趨勢，顧客評論成為醫療機構了解顧客需求的新管道，對於醫院的營運及發展越來越有影響力。隨著網際網路的興盛，線上評論成為顧客發表消費心得及體驗、獲取他人情報的新興管道。因此本研究使用文字探勘技術分析Google地圖上各醫療院所之線上評論，首先以相關分析衡量滿意度指標與醫院財務表現及各項指標的關聯程度及顯著性，再以情緒分析探索文字評論內容之正面及負面情緒，最後，以關聯分析找出造成醫療顧客的正面評論及負面評論之主要因素，探討我國醫療服務之現況。
本研究結果顯示：(1)以Google地圖上的地標星等作為醫療服務滿意度指標，與除了 “醫療結餘” 外的多項醫療機構財務表現呈現顯著的相關性，代表醫療服務品質的提升會增加醫療顧客的回診率以及吸引潛在顧客，增加醫療機構整體收入及利潤。而醫療資源的越充足、機構規模越大，顧客醫療服務滿意度也相對越高。(2) 以ANTUSD結構化用戶文字評論中的情緒後，得知情緒分數與用來衡量顧客滿意度的用戶評定星等呈正相關。即當顧客在醫療服務中的情緒愈正面，滿意程度越高，其給的評定星等也會愈高。(3) 在對顧客文字評論進行關聯分析後，「醫療人員的服務態度」及「醫療過程及等候時間」為影響顧客正面評論及負面評論的兩大主要因素，而「環境設施」及「服務結果」則分別影響正 面及負面評論的第三大因素。

Recently, the medical industry moves towards the "people-oriented" service, because increasing of ageing population, and domestic consumer awareness, market competition in medical services gets fierce in Taiwan. Improving the quality of medical services and customer satisfaction has become the trend of the medical industry, with the development of the Internet, online reviews forums have become an emerging channel for customers to express their consumer experience and obtain information from others, and more and more influence on the operation and development of hospitals. Therefore, this study used text mining technique to analyze the online reviews of various medical institutions on Google Maps. First of all, we adopted the correlation analysis to measure the degree and significance of the correlation among satisfaction indicators, hospital financial performance and other indicators. Secondly, we applied the sentimental analysis to explore the positive and negative emotions of text comments. Finally, we used the association analysis to find the main factors that caused positive and negative comments of medical customers and to explore the current situation of medical services in Taiwan.
The results showed that (1) the medical service satisfaction index taken from the landmark rating star on Google Maps has significantly correlated with many financial performance indicators of the medical institutions except for the medical balance. This suggests that the improvement of medical service quality will increase the revisit rate, the overall income and profits of the hospital, and potential customers. Additionally, with more abundant medical resources and larger institutional scale, there will be higher customer satisfaction with the medical service. (2) After structuring text-based user reviews with ANTUSD, the sentimental score was positively related to user ratings which were used to measure customer satisfaction. In other words, when the customer’s mood in medical services is more positive, the satisfaction and user ratings will be higher. (3) After using the association analysis on text-based user reviews, “Service Attitude of Medical Staff” and “Medical Process and Waiting Time” were two main impacting factors on positive and negative customer comments while “Environmental Facilities” and “Service Outcomes” were the third.

中文文獻
王美慧、陳瑞龍(2005)。醫療服務品質之探討-以花蓮地區醫院為例。管理科學研究。第2卷第1期，35-55頁。
何雍慶、李怡慶(2013)，顧客忠誠度與其前置因素間之關係影響研究 -以醫療服務業為例。中華管理評論國際學報，第十六卷‧第一期。
吳冠陞(2018)。我國主要健保特約醫院財務報表分析。國立中山大學 財務管理學系研究所，碩士論文。
李君儀(2016)。東南亞國際醫療-行銷經驗分享。馬偕院訊 2016年04月(342期)。
良醫健康網，網址：https://health.businessweekly.com.tw/。檢索日期：2020年12月8日。
卓佳慶、劉順仁(2011)。醫療服務品質與財務績效：台灣血液透析診所的實證分析。管理學報，201202 (29:1期)。
社團法人台灣醫務管理學會(2020)。台灣醫療照護品質指標系列。網址：http://www.tche.org.tw/ui/N/N10001.aspx，檢索日期：2020年12月29日。
洪儀珍、陳安琪、丁菱、陳宜君、盛望徽(2018)。醫院環境清潔確效方法之介紹與運用。感染控制雜誌201812 (28:6期)，264-273。
翁瑞宏、黃靖媛、邱柏松(2008)。醫療服務業關係品質與關係利益對顧客忠誠度的影響。中山管理評論16(3)，543-574。
郭俊桔、張育蓉(2013)。使用情緒分析於圖書館使用者 滿意度評估之研究。圖書資訊學研究 7：2 (June 2013)：153-197。
陳素玲(2019)。推行「全面品質管理(TQM)」成為企業的DNA，myMkc.com管理知識中心。網址：https://mymkc.com/article/content/23249，檢索日期：2020年11月21日。
陳敏郎、劉美玉、楊鎮嘉(2011)。就醫民眾對於醫師服務品質之認知的比較研究：以中部兩家區域教學醫院內科門診為例。弘光學報，65:1-16。
陳麗婷、楊惠君(2018)。【外包篇】無所不外包，美食街、停車場開創多少「陪病經濟」？。報導者，刊出日期：2018年11月7日， 網址：https://www.twreporter.org/a/nursing-aide-crisis-of-taking-care-hospital-outsourcing，檢索日期：2020年12月8日。
傳鍾仁、張錫惠(2002)。我國醫療服務品質滿意度之實證研究。台灣管理學刊第1卷第2期。
趙正敏、張宗泓、姚鈺(2018)。精實醫療： 同步提升 品質和效率。哈佛商業評論 全球繁體中文版，2018年2月1日。網址： https://www.hbrtaiwan.com/article_content_AR0007739.html 。檢索日期：2020年11月21日。
蔡文惠、陳昭男、李彩雲、周芳如、李翠芬 (2014)。醫學中心牙科門診醫療服務病患滿意度之分析探討。Taiwan Journal of Oral Medicine Sciences，29(2)，71-85。
衛生福利部全民健康保險會，網址：https://dep.mohw.gov.tw/nhic/mp-116.html。檢索日期：2020年12月22日。
醫聯網，網址：https://expert.med-net.com/index。檢索日期：2020年12月8日。
魏文欽、扈克傑(2008)。醫療服務品質、病患滿意度與忠誠度之關聯性研究--以高高屏地區區域教學醫院為例。International Journal of LISREL，1(2)，25-49。
 
英文文獻
Abubakar, A. M.& Ilkan, M. (2016). Impact of online WOM on destination trust and intention to travel: a medical tourism perspective. Journal of Destination Marketing and Management 5(3), Pages 192–201.
Agrawal, R., Srikant, R.(1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), VOL. 1215. 487-499.
Akdeniz, B., Calantone, R.J., Voorhees, C.M. (2013).Effectiveness of marketing cues on consumer perceptions of quality: The moderating roles of 102 HUANG AND KORFIATIS brand reputation and third-party information. Psychology and Marketing, 30, 76–89.
Akilan, A.(2015)Text mining: Challenges and future directions. 2015 2nd International Conference on Electronics and Communication Systems (ICECS) , pp. 1679-1684.
BrightLocal(2019),Local Consumer Review Survey. 網址：https://www.brightlocal.com/learn/local-consumer-review-survey/ ，檢索日期:2020年11月1號
Burnkrant, R.E.& Cousineau, A.(1975). Informational and normative social influence in buyer behavior. Journal of Consumer Research, 2, 3 (1975), Pages 206–215.
Cantallops, A. S.& Salvi, F. (2014). New consumer behavior: a review of research on eWOM and hotels. International Journal of Hospitality Management, Volume 36, Pages 41-51.
Chen, Y.& Xie, J.(2008), Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix ,March 2008 Management Science 54(3): Pages 477-491.
Coulter, A.(2002).The Autonomous Patient: Ending Paternalism in Medical Care. TSO, London.
Crane, Frederick G. and Jacqueline E. Lynch (1988). "Consumer Selection of Physicians and Dentists: An Examination of Choice Criteria and Cue Usage". Marketing Health Services, 8 (3), Pages 16-19.
Doh, S.-J., and Hwang, J.-S. (2009). How consumers evaluate eWOM (electronic word-of-mouth) messages. Cyber Psychology and Behavior, 12, 2 , 193–197.
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56 (2013),pp. 82-89.
Floyd, K., Freling, R., Alhoqail, S., Cho, H.Y., Freling, T. (2014).How online product reviews affect retail sales: a meta-analysis. Journal of Retailing, 90, pp. 217-232.
Globenko, A., Sianova, Z.,(2012). Service quality in healthcare: quality improvement initiatives through the prism of patients' and providers' perspective. Master thesis. Umeå School of Business and Economics Spring semester 2012.
Goh, D. H., Ang, R. P. (2007). An introduction to association rule mining: An application in counseling and help seeking behavior of adolescents. Journal of Behavior Research Methods,39 (2),259-266.
Gómez-Suárez, M., Martínez-Ruiz, M. P., Martínez-Caraballo, N. (2017). Consumer-brand relationships under the marketing 3.0 paradigm: a literature review. Frontiers in Psychology 8(252) .
Gupta, V., Lehal, G. (2009), A Survey of Text Mining Techniques and Applications. Journal of Emerging Technologies in Web Intelligence, VOL. 1, NO. 1, 60-76.
Hedges, L., Couey, C. (2020), How Patients Use Online Reviews, softwareadvice.com,網址： https://www.softwareadvice.com/resources/how-patients-use-online-reviews/ ,檢索日期：2020年11月30日。
Hennig-Thurau, T., Gwinner, K. P. Walsh, G. and Gremler, D. D. (2004), “Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate themselves on the Internet? “. Journal of Interactive Marketing, Vol. 18, No. 1, Pages 38-52.
Henriksson, A., Zhao, J., Dalianis, H., Bostrom, H. ( 2016). Ensembles of ¨randomized trees using diverse distributed representations of clinical events BMC Medical Informatics and Decision Making vol. 16, no. 2, p. 69.
Hermann, F. (2020). Why Customers Read Google Reviews & How It Impacts Small Businesses? 網址： https://birdeye.com/blog/read-google-reviews/ ，檢索日期：2020年12月4日。
Hong, S. & Park, H.S. (2012) Computer-mediated persuasion in online reviews: statistical versus narrative evidence. Computers in Human Behavior, 28 (3), pp. 906-919.
Jalilvand, M. R.&Samiei, N. (2012). The impact of electronic word of mouth on a tourism destination choice: testing the theory of planned behavior (TPB). Internet Research, 22(5) , Pages 591–612.
Kaplan, A. M. & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), Pages 59-68.
Karimi, S.& Wang, F.. (2017) Online review helpfulness: impact of reviewer profile image. Decision Support Systems, 96 , pp. 39-48.
Ku, Lun-Wei, Ho, Xiu-Wei and Chen, Hsin-Hsi, "Opinion Mining and Relationship Discovery Using CopeOpi Opinion Analysis System," Journal of American Society for Information Science and Technology, volume 60, number 7, pages 1486-1503, July 2009.
Li, N.& Wu, D. ( 2010).Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support System, 48 (2010), pp. 354-368.
Liu, F. & Lu, X. (2011). Survey on text clustering algorithm. In Proceedings of 2nd International IEEE Conference on Software Engineering and Services Science,901–904.
Maslowska, E., Malthouse, E.C., Viswanathan, V. (2017). Do customer reviews drive purchase decisions? The moderating roles of review exposure and price. Decision Support Systems, 98, pp. 1-9
Medhat, W., Hassan, A., Korashy, H. (2014). Sentiment analysis algorithms and applications: a survey. Ain Shams Engineering Journal, Volume 5, Issue 4, Pages 1093-1113.
Oliveira, B.& Casais, B. (2019), “The importance of user-generated photos in restaurant selection”, Journal of Hospitality and Tourism Technology, Vol. 10 No. 1, Pages 2–14.
Owusu-Frimpong, N., Nwankwo, S. & Dason , B.(2010) Measuring service quality and patient satisfaction with access to public and private healthcare delivery. International Journal of Public Sector Management, 23 (3), Pages 203-220.
Pak, A.& Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the International Conference on Language Resources and Evaluation ,Pages 19-21.
Ramanathan, V., & Meyyappan, T.(2013)Survey of text mining. In Proceedings of International Conference on Technology and Business Management. pp. 508–514.
Ravi, K.& Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, 89 (2015), pp. 14-46.
Ren F., Quan C. (2012) Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: An application of affective computing. Information Technology and Management, 13 (4) (2012), pp. 321-332.
Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47( 1), 68-78.
Samsudin, N., Puteh, M., Hamdan, A. R., Nazri, M. Z. A.(2013). Immune based feature selection for opinion mining. Proceedings of the World Congress on Engineering, vol. 3, pp. 3–5.
Smeureanu, I., Delcea, C., Dioşteanu, A., Cotfas, L.A.(2011).Business Ontology for Evaluating Corporate Social Responsibility. Amfiteatru Economic, vol. 29, 2011, pp. 28-42.
Smith, A. & Anderson, M. (2016), Online reviews.網址：http://www.pewinternet.org/2016/12/ 19/online-reviews/ ，檢索日期： 2020年11月01日。
Sower, V., Duffy, J., Kilbourne, W., Kohers, G. & Jones, P.(2001).The Dimensions of Service Quality for Hospitals: Development and Use of the KQCAY Scale. Health Care Management Review,26(2), Pages 47-59.
Statcounter (2020). Search Engine Market Share Taiwan. 網址：https://gs.statcounter.com/search-engine-market-share/all/taiwan，檢索日期：2020年12月4日。
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.
Truitt,L.(2020).The Inside Scoop on Ecommerce Reviews: Why They Matter and How to Make the Most of Them. 網址： https://www.bigcommerce.com/blog/online-reviews/#who-is-reading-online-reviews, 檢索日期：2020年12月4日。
Utz, S., Kerkhof, P., Bos, v. d. J.(2012) Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research and Applications, 49–58.
Vinodhini, G., Chandrasekaran, R.M. (2012). Sentiment analysis and opinion mining: a survey. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, 282-292.
Wang, Shih-Ming and Ku, Lun-Wei. ANTUSD: A large chinese sentiment dictionary. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, may 2016, pages 2697-2702.
Weiguo, F., Wallace, L., Rich, S., Zhongju, Z.(2006). Tapping the Power of Text Mining. Communication of the ACM,49(9), 77-82.
Wong, E., Coulter A., Hewitson P., Cheung,A., Yam, C. et al. (2015).Patient Experience and Satisfaction with Inpatient Service：Development of Short Form Survey Instrument Measuring the Core Aspect of Inpatient Experience. PLoS ONE, 10(4): Pages 122-299.
Woodruff, R. B., Cadotte E. R. & Jenkins R. L. (1983). Modeling Consumer Satisfaction Process Using Experience Based Norms. Journal of Marketing, 20, 296-304.
Wu, P.F.(2013),In search of negativity bias: an empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30 pp. 971-984
Yang, J. & Mai, E.S.(2010) Experiential goods with network externalities effects: An empirical study of online rating system. Journal of Business Research, 69, 1050–1057.
Ye, Q., Zhang, Z., Law, R.,(2008).Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, Volume 36, Issue 3, Part 2, Pages 6527-6535.
Yu, D., Mu, Y., Jin, Y., (2017). Rating prediction using review texts with underlying sentiments. Information Processing Letters, Volume 117 , Pages 10-18.
Ziegele, M.& Weber, M. (2015). Example, please! Comparing the effects of single customer reviews and aggregate review scores on online shoppers' product evaluations. Journal of Consumer Behaviour, 14, pp. 103-114.