Evaluation of Public Perception of Commercial Pedestrian Streets Based on UGC Data: A Case Study of Chongqing, China

Vollständiger Abstract

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Against the backdrop of high-density Asian cities shifting from production- to consumption-oriented spaces, commercial pedestrian streets are key to urban public life and vitality. This study selects six major commercial pedestrian streets in Chongqing and employs natural language processing (NLP) and importance–performance analysis (IPA) methods to construct a four-dimensional evaluation framework (spatial, commercial, cultural, location/facility). It analyzes public perception and experience based on user-generated content (UGC). Findings show that: (1) Significant differences across dimensions form three development types: cultural identity, functional hub, and distinctive growth, reflecting structural bottlenecks in transitioning from single- to multi-functional spaces. (2) IPA identifies “business formats,” “cultural activities,” and “consumption experience” as priorities for improvement, while “commercial atmosphere” and “transportation conditions” are current strengths to maintain. (3) Sentiment analysis reveals that negative perceptions focus on basic functions and sense of place, whereas positive sentiments relate to cultural expression and spatial esthetics, highlighting the role of cultural soft power and visual design in street appeal. This study reveals public perception patterns via big data analysis, offering empirical support for the refined renewal, cultural preservation, and sustainable management of commercial pedestrian streets in high-density Asian cities.