시멘틱 네트워크 분석을 활용한 오버투어리즘 인식 규명 및 해소방안에 관한 연구

초록

The purpose of study was to recommend the solution plans of overtourism and perception of tourist’s by social big data. Naver, Daum and Goggle were collected as analysis social channels. The 2,445 words were extracted as through semantic network analysis with a keyword of ‘overtourism’ for big data research. The period was limited for 3 years from November 15, 2016 to November 14, 2019. The data were text mining by textom and Krkwicm, UCINET program for social network analysis, NodeXL was to visualize the network between words linked to the keyword. As a results, first, various keywords recognized by tourist’s such as ‘tourist’, ‘resident’, ‘problem’, ‘jeju’, ‘trip’, tourist attraction‘’, ‘tourism’, ‘city’, ‘life’, ‘consumer’, ‘phenomenon’, ‘receptive’, ‘trend’, ‘region’, ‘social’, ‘world‘ were extracted. Second, as the result of cluster group analysis, totally 6 big groups were formed. Ultimately, the solution of overtourism will be able to achieve sustainable virtuous circle tourism that solves the quantitative problems of tourism and improves the quality of tourism through win-win cooperation with policy efforts and administrative supplementation and maintenance.

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