비정형 빅데이터를 활용한 호텔 패키지 상품에 대한 고객 인식 조사: 코로나 19 팬데믹 기간 = A study on the customer perceptions of hotel package products using unstructured big data: The COVID-19 pandemic period

다국어 초록 (Multilingual Abstract)

This study performed a big data analysis using text mining to identify trends among customers regarding the use of hotel packages under the ongoing COVID-19 situation. To this end, Textom, a textual data batch solution program, was used to derive keywords related to hotel packages through Naver, Google, and Daum. The collection period was set from September 2020 to August 2022, a period when COVID-19 spread worldwide. A total of 10,749 keywords were derived, and the final 70 primary keywords were used in the empirical analysis of this study through the purification process. First, frequency and TF-IDF analysis presented in order: Seoul hotel, product, hokangs, and pool. Next, after performing the centrality analysis, the connection centrality was shown in the order of: Seoul hotel, product, hokangs, pool, central proximity, was reservation on Naver.com, Myeongdong, glad hotel, maximum discount. The intermediary centrality was shown in the order of: discount, pool, reservation, accommodation, and event. Finally, after performing a CONCOR analysis that rearranged and grouped highly correlated words, a total of four groups were found: package reservation attributes, package use plans, hotel and regional selection attributes, and hotel package product attributes. Through these analyses, we identified customer trends and key components related to hotel packages. This study also suggests directions for future research related to hotel package trends and provides implications for the implementation of competitive marketing strategies.