빅데이터를 활용한 웰니스 관광에 대한 인식과 태도 분석

Abstract

This study used big data to conduct text mining and sentiment analysis on individuals' perceptions and attitudes toward wellness tourism. From October 8th, 2018 to October 7th, 2021, web documents containing keywords such as 'wellness tourism' and 'wellness travel' were collected from Naver, Daum, and Google. A total of 23,770 documents were used to analyze keywords frequency, TF-IDF, N-gram, degree centrality, CONCOR, and sentiment analysis after duplicated posts and meaningless words were removed. The findings revealed that the words most frequently appearing and having high TF-IDF values were mentioned in the context of wellness tourism destination selection and development, purposes and types of wellness travel, and information on domestic wellness tourist spots. Words associated with wellness, tourism, tourism destinations, selections, and so on played critical roles in the network, according to degree centrality measures. Six clusters emerged from CONCOR analysis, including core attributes, projects, wellness tourism development, and so on. Finally, sentiment analysis revealed that people had a generally positive impression of wellness tourism. This study contributes to a better understanding of wellness tourism perceptions and aids in the development of marketing strategies to revitalize wellness tourism.