Abstract
The purpose of this study was to analyze consumption trend in ski resorts before and after COVID-19 using social big data. Toward this end, a big data solution, "TextoM" was utilized to crawl and analyze social text data related to ski resorts from NAVER, DAUM, and Google. Data analyses included text mining, sentiment analysis, and semantic network analysis. The results of this study were as follows. Firstly, the result of text mining indicated that ski and snowboard participants preferred taking a one-day ski trip to staying one or two nights at the ski resorts after COVID-19. In addition, safety-related keyword increased after COVID-19. Secondly, sentiment analysis revealed that negative emotion about ski resorts has been increasing since COVID-19 outbreak. Thirdly, network among ski resorts related keyword was loosen after COVID-19, indicating that ski and snowboard participant"s interest in ski resorts decreased. Discussions and additional practical implication were also suggested.
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