用用户生成文本和GPT-4o评估社区住房质量，准确率达92.5%。
Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework
- 融合多平台文本，用GPT-4o提取结构化评价单元并打分。
- 构建46项指标体系，发现主观与客观评估存在差异。
- 适合城市规划、政策制定者参考，可扩展至其他城市评估。
本研究利用GPT-4o分析来自大众点评、微博和政府留言板的多源文本型用户生成内容（UGC），以评估社区住房质量。通过筛选相关文本、提取结构化评价单元并进行情感评分，构建了包含11个类别共46项指标的住房质量评估体系。结果揭示了客观与主观评估间的差距，以及不同平台关注重点的差异。在微调设置下，GPT-4o达到92.5%的准确率，优于基于规则和BERT的模型。研究证明，整合UGC与GPT驱动分析，可实现可扩展、以居民为中心的城市评估，为政策制定者和城市规划提供实用洞见。
原文摘要 · Abstract (English)
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.
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