運用文字探勘分析全民健保與五項總額支付制度之民眾知覺感受

Application of text mining in the public perception analysis of global budget payment and National Health Insurance systems

10.6288/TJPH.201904_38(2).107137

江婉琪(Wan-Chi Chiang)；林應龍(Ying-Lung Lin)；禹良治(Liang-Chih Yu)；張鈺旋(Yu-Hsuan Chang)；陳俞安(Yu-An Chen)；王復中(Fu-Chung Wang)；黃珮珊(Pei-Shan Huang)；林寬佳(Kuan-Chia Lin)

全民健保與五項總額支付制度 ； 情感分析 ； 文字探勘 ； 公眾感受 ； National Health Insurance (NHI) and five global budget payment system ； text mining ； sentiment analysis ； public perception

台灣公共衛生雜誌

38卷2期（2019 / 04 / 01）

189 - 202

繁體中文

目標：本研究為發展與評估一套健保文字探勘技術以彌補傳統量性監測工具的不足並克服質性資料分析與解讀上的困難。方法：以106年度全民健保與五項總額支付制度之全國民意調查為基礎。運用文字採礦技術探討民眾對於健保意見感受之語意結構。進一步將上述語意結構進行情感分類，評估其做為輔助監測民意之適切性。結果：六大文本的詞頻統計皆近似齊夫經驗法則，少數高頻詞彙影響多數的文本內容。透過文字雲得到共有語意特徵為「很好」、「滿意」和「不錯」等對健保正向的評價。於因素分析皆呈現多元的潛在語意結構，整體解釋變異達70％以上。情感標記圖之分布皆呈反拋物線，點座落位置與色彩間的對比差異說明質、量性資料彼此具互補效益。結論：本研究具體實證如何從公眾的自由評論中，提取出輿情感受與意向，同時藉由情感標記，偵測文字評論背後的情感與情緒，將可作為探討民眾對於健康政策感知的另一項調查方法依據。

Objectives: The aim of this study was to facilitate text mining for overcoming the limitations of traditional questionnaire-based surveys and the challenges of analyzing textual data for monitoring public perceptions regarding the different fields of the National Health Insurance (NHI) system. Methods: The study data consisted of the 2017 national survey and public polls regarding people's satisfaction with NHI and five global budget payment systems in Taiwan. We derived the structure and information from the text of responses through text mining and further applied sentiment analysis by using lexical signifiers and classifying emotions to determine whether the data lean toward positive or negative emotions. Results: (1) The rank-frequency distribution for all texts of the six major fields of medical care followed Zipf's law. This means that a few high-frequency words affected most of the textual content. (2) The results in word cloud visualizations could reflect public perceptions regarding various topics. The most common semantic features were ＂very well,＂ ＂satisfied,＂ and ＂good.＂ Most participants, especially dialysis patients, provided positive comments on different types of medical care in the NHI system, which shows that the health care offered by the NHI system meets peoples' needs. (3) Factor analysis further indicated that our semantic results explained more than 70% of the variance for the six major texts. For the majority of the dialysis patients, the semantic structure of responses consisted of positive words and was significantly different from those of other texts. (4) The reverse para-curve and color differences in valence-arousal space further produced qualitative and quantitative results indicating the subjects' positive perceptions and opinions. Conclusions: Public perceptions and opinions are essential for policy evaluation and implementation. They play a critical role in policy innovations. This study outlines the potential benefits of text mining and sentiment analysis techniques for developing an NHI sentiment analysis system that can aid in the assessment of public perceptions and opinions regarding the health care system. Police surveillance can benefit by incorporating such a systematic approach into the visualization and standardization of data content.

1. Berezina, K,Bilgihan, A,Cobanoglu, C,Okumus, F(2016).Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews.J Hospit Market Manag,25,1-24.
2. Bradley, MM,Lang, PJ(1994).Measuring emotion: the self-assessment manikin and the semantic differential.J Behav Ther Exp Psychiatr,25,49-59.
3. Cormack, GV(2008).Email spam filtering: a systematic review.Foundations Trends Information Retrieval,1,335-455.
4. Davis, MA,Zheng, K,Liu, Y,Levy, H(2017).Public response to obamacare on twitter.J Med Internet Res,19,e167.
5. GitHub. Chinese text segmentation with R. Available at: https://github.com/qinwf/jiebaR/. Accessed December 28, 2018.
6. Greaves, F,Ramirez-Cano, D,Millett, C,Darzi, A,Donaldson, L(2013).Use of sentiment analysis for capturing patient experience from free-text comments posted online.J Med Internet Res,15,e239.
7. Han, J,Kamber, M,Pei, J(2012).Data Mining: Concepts and Techniques.Waltham, MA:Morgan Kaufmann.
8. Hawkins, JB,Brownstein, JS,Tuli, G(2016).Measuring patient-perceived quality of care in US hospitals using Twitter.BMJ Qual Saf,25,404-413.
9. Huppertz, JW,Otto, P(2018).Predicting HCAHPS scores from hospitals’ social media pages: a sentiment analysis.Health Care Manage Rev,4,359-367.
10. Institute of Medicine (US) Committee on Quality of Health Care in America(ed),Kohn, LT(ed),Corrigan, JM(ed),Donaldson, MS(ed)(2000).To Err is Human: Building a Safer Health System.Washington, DC:National Academies Press.
11. Lang D, Chien GT. Package ‘wordcloud2’. Available at: https://cran.r-project.org/web/packages/wordcloud2/wordcloud2.pdf. Accessed January 3, 2018.
12. Liu, B.(2012).Sentiment analysis and opinion mining.Synthesis Lectures on Human Language Technologies,5,1-167.
13. Mostafa, MM(2013).More than words: social networks’ text mining for consumer brand sentiments.Expert Syst Appl,40,4241-4251.
14. Rosenkrantz, AB,Hawkins, CM(2017).Use of Twitter Polls to determine public opinion regarding content presented at a major national specialty society meeting.J Am Coll Radiol,14,177-182.
15. Salton, G,Buckley, C(1988).Term-weighting approaches in automatic text retrieval.Inform Process Manag,24,513-523.
16. Srivastava, AN,Sahami, M.(2009).Text Mining: Classification, Clustering, and Applications.England, UK:Chapman and Hall/CRC.
17. Wang, H,Can, D,Kazemzadeh, A,Bar, F,Narayanan, S(2012).A system for real-time twitter sentiment analysis of 2012 US presidential election cycle.Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics of Association for Computational Linguistics,Korea: Jeju, Island:
18. Weiss, SM,Indurkhya, N,Zhang, T(2015).Fundamentals of Predictive Text Mining.London, UK:Springer.
19. Xiao, H(2008).On the applicability of Zipf's Law in Chinese word frequency distribution.J Chinese Language Computing,18,33-46.
20. Yu, LC,Lee, LH,Hao, S(2016).Building Chinese affective resources in valence-arousal dimensions.Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,San Diego, CA:
21. 林師模, SM,陳苑欽, YC(2013).多變量分析：管理上的應用.台北=Taipei:雙葉書廊=Yeh Yeh Book Gallery.
22. 林寬佳, KC,邱尚志, SJ,李麗惠, LH(2015)。,台北=Taipei:衛生福利部中央健康保險署=National Health Insurance Administration, Ministry of Health and Welfare, R.O.C. (Taiwan)。
23. 林寬佳, KC,鄧宗業, CY,李佩珍, PC(2017)。,台北=Taipei:衛生福利部中央健康保險署=National Health Insurance Administration, Ministry of Health and Welfare, R.O.C. (Taiwan)。
24. 竺家寧, CN(2005).語言風格與文學韻律.台北=Taipei:五南圖書出版有限公司=Wu-Nan Book Inc..
25. 陳怡君, YC,黃淑齡, SL,施悅音, YY,陳克健, KJ。,未出版
26. 楊銘欽, MC,董鈺琪, YC,譚家惠, CH(2013)。保險對象就醫權益監測模式之先驅研究。2013台灣公共衛生學會聯合年會，台北=Taipei:
27. 衛生福利部中央健康保險署, Ministry of Health and Welfare, R.O.C. (Taiwan)(2016).2016-2017年全民健康保險年報.台北=Taipei:衛生福利部中央健康保險署=National Health Insurance Administration, Ministry of Health and Welfare, R.O.C. (Taiwan).

1. 程湲晏,陳祐祥,曹慧華,丁敏慧(2020)。先進分類模式於保費數據分析之應用。管理資訊計算，9(特刊2)，112-120。
2. (2023)。台灣小區域民眾就醫經驗之分析。台灣公共衛生雜誌，42(6)，651-662。