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Analyzing user-generated content using natural language processing: a case study of public satisfaction with
1Research Institute for Work and Society, University of Leuven, Parkstraat 47, Box 5300, 3000 Leuven, Belgium.
Journal of Computational Social Science
|November 3, 2021
Summary
This study analyzes online comments to gauge public satisfaction with healthcare systems using machine learning. It ranks national healthcare systems by sentiment, offering insights beyond traditional surveys.
Area of Science:
- Computational Social Science
- Natural Language Processing
- Health Services Research
Background:
- Public opinion studies often overlook the vast potential of user-generated online content (UGC).
- Machine learning and text mining offer novel methods for analyzing large volumes of online data.
- Understanding public sentiment towards healthcare systems is crucial for service improvement.
Purpose of the Study:
- To explore the utility of UGC for public opinion analysis in healthcare.
- To apply natural language processing (NLP), text mining, and sentiment analysis to online comments on healthcare.
- To classify and rank national healthcare systems based on public sentiment expressed in UGC.
Main Methods:
- Collected and analyzed 634 online comments regarding healthcare services.
- Employed NLP techniques for text pre-processing and topic frequency identification.
- Utilized sentiment analysis to derive public satisfaction scores and rank healthcare systems.
Main Results:
- Identified key topics and sentiment distributions within online healthcare discussions.
- Developed a method for ranking national healthcare systems based on UGC sentiment.
- Compared findings with traditional cross-national survey data.
Conclusions:
- UGC provides a valuable, albeit complex, data source for public opinion on healthcare.
- Machine learning techniques can effectively extract sentiment and rank systems from online text.
- Further research is needed to address limitations of UGC analysis.
Keywords:
Computational social scienceHealthcare systemsPublic attitudesSentiment analysisText miningUser-generated online contentMore Related Videos
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