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

• Published in: Journal of Wound Management and Research Vol. 20; no. 3; pp. 251 - 260
• Main Authors: Park, Kyung Hee, Lee, Jinho, Kwon, Soon Chul, Kim, Jaeseung
• Format: Journal Article
• Language: English
• Published: 대한창상학회 31.10.2024
• Subjects: 의학일반
• ISSN: 2586-0402; 2586-0410
• DOI: 10.22467/jwmr.2024.03069

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.