Uncovering Drivers of Resident Satisfaction in Urban Renewal: Contextual Perception Mining of Old Community Regeneration Through Large Language Models

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Abstract
Urban regeneration has increasingly become a global strategy for promoting sustainable urban development, with the renewal of deteriorating residential communities serving as a key dimension of this process. Within the framework of a people-centered development paradigm, growing attention has been directed toward the necessity of securing residents’ satisfaction in community renewal initiatives. This study employs advanced textual analysis of resident submissions collected from government–citizen interaction platforms to investigate the determinants of satisfaction with renewal projects. Leveraging the semantic comprehension capabilities of large language models (LLMs), we identify both salient keywords and sentiment orientations embedded in residents’ narratives. Guided by the theoretical framework of resident satisfaction, the extracted keywords are organized into seven thematic domains: basic infrastructure improvement, quality-enhancement renovation, solicitation of residents’ preferences, residents’ decision-making power, policy transparency, construction governance, and community-level communication. Regression modeling is subsequently applied to assess the relative influence of these thematic domains on residents’ satisfaction. The findings suggest that insufficient integration of residents’ preferences at the preliminary stages of participation constitutes a principal source of dissatisfaction during the implementation of renewal projects. Furthermore, the study compares Latent Dirichlet Allocation (LDA) topic modeling with LLMs-based topic clustering, revealing the latter’s superior capacity to capture thematic structures in complex, long-form textual data. These results underscore the potential of LLMs to enhance the analytical rigor of research on urban regeneration and citizen participation.

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