초록 열기/닫기 버튼
This study investigates service quality perceptions of airlines before and after COVID-19 pandemic using topic modeling and semantic association analysis. To this end, this study was conducted by collecting customer’s reviews of 4 airlines selected by TripAdvisor, and total 15,716 reviews were collected. The data was followed by text preprocessing, LDA (latent dirichlet allocation) using Python, then semantic association analysis by Python and Gephi. Through LDA, one of the topic modeling methodologies was confirmed that ground service, in-flight service, service consistency, frequent-flyer, business class, connection, entertainment, reservation, food and beverage, legroom, human service, and economy class were the topics before the COVID-19 pandemic. After the COVID-19 pandemic, the topics were selected as reservation service, human service, quarantine, ground service, seat class, refunds, and changes. In addition, we found that words related to in-flight service such as flight, class, service, food, and seating were strongly linked before the COVID-19 using semantic association analysis. But after the COVID-19 pandemic, the quality of ground service related to ticket reservation, refund, cancellation was important.
키워드열기/닫기 버튼
COVID-19, Airline, Service Quality, Topic Modeling, Semantic Association Analysis
