Templestay: Exploring Authenticity Dimensions Through Place Experience by LDA Topic Modeling

With the growth of the wellness tourism industry, cultural tourism destinations offering authentic experiences for well-being are attracting increasing attention. In this context, this study aims to explore the place experiences of Templestay, Korea’s representative cultural and authenticity-seeking tourism destination, through an analysis of participant reviews. It seeks to contribute to strengthening Templestay’s spatial characteristics as a destination pursuing authenticity. Unstructured data were collected from portal sites and SNS travel reviews of Templestay, with 22,736 pieces of data analyzed through text refinement. Text mining and latent Dirichlet allocation (LDA) topic modeling analysis were employed on the collected textual big data. The LDA topic modeling analysis revealed two topics related to Templestay’s place experiences: spatial activity and physical environment, highlighting tourists’ perception of authenticity. Leveraging these findings in destination marketing strategies for Templestay can offer customized information for authenticity pursuits and enhance its appeal as cultural tourism. This study contributes to the theoretical understanding of tourism studies and the development of the wellness tourism industry as a sustainable tourism model by empirically analyzing the place experiences of Templestay as a tourism destination oriented toward authenticity using big data.