Visitor Experiences in Urban Night Tourism: Insights from Big Data on Hongyadong, Chongqing

Abstract

Night tourism is an increasingly prominent component of urban cultural economies, yet the experiential structure of visitors' night-time encounters remains underexplored. This study examines visitor experiences in Hongyadong, a flagship night tourism cluster in Chongqing, China, using large-scale user-generated content from Ctrip. Online reviews posted between 2021 and 2025 were analyzed through a multi-stage text-mining procedure comprising word frequency analysis and co-occurrence mapping in KH Coder, followed by Latent Dirichlet Allocation (LDA) topic modelling in Python. The results reveal nine interrelated experiential themes, encompassing nightscape aesthetics, viewing and photography practices, river-based mobility, local cultural symbolism, food and commercial experiences, service and accessibility, and perceived value. These themes portray urban night tourism as a multisensory and spatially dynamic experience shaped by the interaction of aesthetic appeal, behavioral engagement and operational conditions. The study advances night tourism research by demonstrating the potential of big-data textual approaches and by providing conceptually grounded insights for planning and managing urban night-time environments.