Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems

While user-generated online content (UGC) is increasingly available, public opinion studies are yet to fully exploit the abundance and richness of online data. This study contributes to the practical knowledge of user-generated online content and machine learning techniques that can be used for the analysis of UGC. For this purpose, we explore the potential of user-generated content and present an application of natural language pre-processing, text mining and sentiment analysis to the question of public satisfaction with healthcare systems. Concretely, we analyze 634 online comments reflecting attitudes towards healthcare services in different countries. Our analysis identifies the frequency of topics related to healthcare services in textual content of the comments and attempts to classify and rank national healthcare systems based on the respondents' sentiment scores. In this paper, we describe our approach, summarize our main findings, and compare them with the results from cross-national surveys. Finally, we outline the typical limitations inherent in the analysis of user-generated online content and suggest avenues for future research.