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빅데이터 분석을 활용한 성형 의료관광에 대한 코로나 19 전후의 인식 비교 연구

Comparative Study of Perceptions of Plastic Surgery Medical Tourism Before and After COVID-19 Utilizing Big Data Analysis

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초록

기술의 발전으로 성형 의료관광의 수요가 늘고 있으나, 코로나바이러스감염증-19(코로나 19) 발생 후 국내 의료관광객의 수가 크게 감소하여 위기를 맞이하였다. 따라서 본 연구는 빅데이터 분석을 활용하여 코로나 19 전후의 성형 의료관광의 인식과 경향을 파악하고, 의료기관에 마케팅 기초 자료 및 정보를 제시하기 위해 수행되었다. 이를 위해 코로나 19 발생 전 3년과 발생 후 3년간의 ‘성형+의료관광’ 키워드로 네이버 뉴스 데이터를 텍스톰(Textom)을 활용하여 수집하여 단어들을 정제하고 텍스트 마이닝을 실시하였다. UCINET 6.0을 이용하여 중심성 분석과 CONCOR 분석을 하고 시각화하였다. 그 결과, 코로나 19전에는 Tourism, Medical, Marketing, Location으로 4개의 클러스터가 생성되었으며 코로나 19 후에는 COVID-19 클러스터가 추가되어 총 5개로 구성되었다. 코로나 발생 전에는 해외 국가명이 높은 빈도를 보였으나, 코로나 발생 후에는 코로나, 방역, 예방 등과 같은 코로나 19와 관련된 단어들이 자주 나타났다. 이러한 결과를 기반으로 코로나 19 이전과 이후의 인식 차이와 변화를 고려한다면, 포스트 코로나 시대에 유용한 마케팅 전략을 제시할 수 있을 것이다.

Abstract

Medical tourism is experiencing an increase in demand due to the advancement of technology in the field of plastic surgery. Since the outbreak of COVID-19, there has been a dramatic decrease in the number of domestic medical tourists, leading to a crisis. By using big data analytics, this study aimed to identify the perception and trend of cosmetic medical tourism before and after COVID-19, as well as present strategic marketing information and basic marketing data to medical institutions. In this study, the Naver news data was analyzed and refined for three years before and after the outbreak of COVID-19, with the keywords of 'plastic surgery + medical tourism', as well as text mining to identify trends. The centrality analysis and CONCOR analysis were performed and visualized utilizing UCINET 6.0. Four clusters were created for the data before COVID-19: Tourism, Medical, Marketing, and Location, and after COVID-19, a COVID-19 cluster was added to make a total of five clusters. Prior to the outbreak of COVID-19, foreign countries' names were in high frequency, but after the outbreak of COVID-19, words associated with COVID-19, such as Corona, Quarantine, and Prevention, were in high frequency. On the basis of these results, suggestions were made based on the differences in cognition that existed from before and after the COVID-19.

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