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

Anna Ruelens ()
Additional contact information
Anna Ruelens: University of Leuven

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 31, 749 pages

Abstract: Abstract 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.

Keywords: User-generated online content; Sentiment analysis; Text mining; Healthcare systems; Public attitudes; Computational social science (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-021-00148-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00148-2

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-021-00148-2

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().