J Wound Manag Res.  2024 Oct;20(3):251-260. 10.22467/jwmr.2024.03069.

Big Data Analysis on Consumer Perception of Pressure Injuries: Text Mining and Semantic Network Analysis

• Park KH
• Lee J
• Kwon SC
• Kim J

• KMID: 2560425
• DOI: http://doi.org/10.22467/jwmr.2024.03069

Abstract

Background
With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data.
Methods
This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis.
Results
Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease.
Conclusion
Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

Keyword

Pressure ulcer; Big data; Text mining; Consumer health information