Assessing the Job Satisfaction of Registered Nurses Using Sentiment Analysis and Clustering Analysis

Matthew Jura, Joanne Spetz, Der Ming Liou

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Job satisfaction is a critical component of the professional work environment and is often ascertained through surveys that include structured or open-ended questions. Using data from 24,543 respondents to California Board of Registered Nursing biennial surveys, this study examines the job satisfaction of registered nurses (RNs) by applying clustering analysis to structured job satisfaction items and sentiment analysis to free-text comments. The clustering analysis identified three job satisfaction groups (low, medium, and high satisfaction). Sentiment analysis scores were significantly associated with the job satisfaction groups in both bivariate and multivariate analyses. Differences between the job satisfaction clusters were mostly driven by satisfaction with workload, adequacy of the clerical support services, adequacy of the number of RN staff, and skills of RN colleagues. In addition, there was dispersion in satisfaction related to involvement in management and policy decisions, recognition for a job well done, and opportunities for professional development.

Original languageEnglish
Pages (from-to)585-593
Number of pages9
JournalMedical Care Research and Review
Issue number4
Publication statusPublished - Aug 2022


  • California
  • job satisfaction
  • nursing
  • qualitative analysis
  • quantitative analysis
  • sentiment analysis

ASJC Scopus subject areas

  • Health Policy


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