Understanding nurses’ challenging work experiences: a text mining-based sentiment analysis and topic modeling study
Year: 2026
Type: article
Abstract: Nurses experience substantial psychological burden during clinical practice. Previous studies have provided limited insights into the emotional dimensions of these experiences and lack specificity in identifying task-level contributors to nursing work experiences. To identify and categorize challenging nursing work experiences using a systematic text-based approach. This study analyzed a subset of data from the SMART NURSE project, comprising 1,650 free-text responses from nurses working in two general hospitals in South Korea. Sentiment analysis was conducted to quantify emotional polarity using a lexicon-based approach. Keyword analysis and Latent Dirichlet Allocation topic modeling were applied to identify the key terms and underlying themes. Topic prevalence and C v coherence were assessed to evaluate the model adequacy. Overall, the sentiment was skewed toward negative emotions. The most frequently mentioned word was “response.” Topic modeling revealed three distinct themes: concurrent task demands, patient flow, and communication load. Concurrent task demands showed the highest prevalence and C v coherence. The most challenging nursing work experiences are characterized by the interplay of concurrent task demands, patient flow, and communication-related burdens. Integrating emotional and contextual dimensions through text mining provides a more comprehensive understanding of nursing work experiences and supports the development of targeted workload management strategies and context-sensitive interventions.
Source: BMC Nursing
Authors: Younhee Kang, Inkyung Park, Suemin Kim
Institution: Ewha Womans University
Language: English
Subfield: Research and Theory
Field: Nursing
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W7222637544
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