To accurately identify characteristics of the tourist experience, optimize tourism management and shape urban tourism brands, this study uses Wuhan as a case and aggregates multimodal user-generated content (UGC) data including tourist reviews, photos and travel vlogs. Based on the “Behavior–Cognition–Affect” framework and the progressive “Region–Route–Site” spatial perspective, this study adopts spatial analysis, image analysis, semantic network analysis, and natural language processing (NLP) to examine tourists’ spatial behavior patterns, visual cognitive preferences, and emotional feedback across urban, attraction, and individual tourist scales. Results show that Wuhan’s tourism presents a “core-periphery” spatial structure, tourists’ visual focus differs significantly across scenic types, and tourists’ emotions are generally positive, with consumption, shopping, and transportation as main negative sources. This study enriches the application of multimodal UGC in tourism geography, providing data to optimize tourism resource allocation and shape urban tourism images.
Li et al. (Mon,) studied this question.
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