The Factors of Exhibition Viewing Experience Using Visitors’ Reviews as Big Data

Abstract

Background Visitors’ experiences of modern exhibitions are various using five senses through physical servicescape and social servicescape of exhibitions, and they tend to write a lot of reviews. However, even though marketing is considered important in the field of exhibition, there are few attempts to analyze reviews from visitors as big data. This study tried to analyze the visitor’s reviews and identify which servicescape factors influence the visitor’s experience. Methods This study tried to identify which factors affect visitors’ exhibition experiences through review analysis by collecting 335 exhibitions data and 27,234 reviews crawled of the exhibitions ranked on the annual exhibition ranking pages provided by Interpark. Three methods of analyzing the reviews which used in this study were WordCloud, Topic modeling based on latent Dirichlet allocation (LDA), and the similarity measurement between words using the Word2vec model. Results As a result, fourteen influential factors were found: docent, audio guide, exhibition medium, visual beauty, visitors’ taste, the density of space, etc. Eleven of these factors were related to information delivery, exhibits, and exhibition environment among factors of servicescape provided by exhibitions. Conclusions This study analyzed visitors’ reviews to identify visitors’ experiences and presented a methodology to research exhibitions even in difficult situations like the COVID-19 pandemic. In addition, this study revealed important factors to be considered when planning and designing exhibitions in terms of the importance of exhibition operations, a waiting system for admission, and MD products, etc. Words extracted from this study can be useful resources to design an exhibition.