Leveraging large language models for tourism research based on 5D framework: A collaborative analysis of tourist sentiments and spatial features

Abstract

Experience-oriented travel models have posed new demands for optimizing urban environments to promote tourism development. This study introduced a natural language classification and scoring method to explore the relationship between tourism experiences and spatial characteristics. We found that online textual data can infer and represent physical spatial features. Our findings include: (1) Tourists perceive density from moving objects, with threshold effects caused by their temporal instability. (2) Ecological and cultural-technological tourism models have varied dependencies on transportation facilities. (3) Central areas dominated by artificial functions and landscapes require more natural planning approaches to enhance the tourist experience. (4) Accessibility perceptions are influenced by driving time and proximity to the city center, rather than walking duration or the actual distance. (5) The development of a dual-network policy for buses and subways is crucial to enhance the travel experience. Our study provides evidence-based recommendations for urban renewal to improve tourism experiences.