Comparison of Nurses’ Image in Korean Online Newspaper Articles before and after COVID-19: A Text Mining Analysis

Background/Objectives: This study investigated nurse-related keywords which were presented together with nurse on phrase, clauses or sentence of documents or conversations in the Korea online newspaper articles. Methods/Statistical analysis: Text mining, which is a common method of analysis for large datasets, was used to analyze the changes in nurses’ image before and after COVID-19. A total of the linking words with nurse were calculated by the number of presentation on online newspaper article in Naver before and after COVID-19. In order to identify the meaning of the words, clustering of the collected linking words by categories was analysed and the characteristics of each cluster were classified. Over 5,000 articles were identified as targets for a keyword analysis. Findings: The most frequently presenting words were hospital, medical and patient. Before COVID-19, the words related to ‘workplace bullying’ were highly presented and after COVID-19, the words related to ‘care’ were highly presented. Improvements/Applications: With analyzing the trends of changes and characteristics of words by COVID-19 and clusters, we attempted to investigate the image of nurse that the public think and feel about nurse.