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

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.