Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework
This provides a scalable, resident-centric method for urban housing assessments, offering practical insights for policymakers and urban planners, though it is incremental in applying existing AI to a new domain.
This study used GPT-4o to assess neighborhood housing quality by analyzing multi-source user-generated text from platforms like Dianping and Weibo, developing a system with 46 indicators. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings.
This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings. The findings underscore the value of integrating UGC and GPT-driven analysis for scalable, resident-centric urban assessments, offering practical insights for policymakers and urban planners.