A data-driven analysis of experience in urban historic districts

This paper implements a context-based text mining approach in online reviews of three European historic city districts, to assess the data-driven projection of experiences and major determinants of traveler satisfaction. Bivariate analyses, semantic networks of experience concepts, and word association statistics are used. Results are interpreted in contrast to experience deterioration issues caused by the loss of authenticity, as argued by recent literature. Overtourism signs are a powerful determinant of unfavorable evaluations, but images formed by reviewers are predominantly positive. Nonetheless, although references to authenticity and local identity are not common, destinations failing to project non-touristified aspects are less favored. Overall, evaluations are driven by gratification from offered products and services, as well as passive esthetic consumption of surroundings.