Big Data Analysis on Consumer Perception of Pressure Injuries: Text Mining and Semantic Network Analysis
- Author:
Kyung Hee PARK
1
;
Jinho LEE
;
Soon Chul KWON
;
Jaeseung KIM
Author Information
- Publication Type:Original Article
- From: Journal of Wound Management and Research 2024;20(3):251-260
- CountryRepublic of Korea
- Language:English
-
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.
