Enterprise AI Analysis
AI-Driven Exploration of Public Perception in Historic Districts Through Deep Learning and Large Language Models
This study introduces an AI-driven framework leveraging deep learning (BERTopic, BERT) and large language models (LLMs) to analyze public perceptions of historic districts, using Qinghefang Historical and Cultural District in Hangzhou as a case study. By processing large-scale online reviews, the framework identifies core perceptual dimensions (urban tourism, cultural ecosystem, food/dining, residential preferences) and quantifies sentiment polarity. The findings reveal a dual identity for historic districts: a highly positive "tourist layer" driven by cultural and culinary experiences, and a more neutral "resident layer" focused on utilitarian aspects like housing. This methodology provides actionable, data-informed insights for heritage management, balancing conservation with modernization and optimizing visitor experience.
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6,468
Total Reviews Analyzed
96.27%
Sentiment Classification Accuracy
66
Topics Identified
5
Perceptual Dimensions Discovered
Deep Analysis & Enterprise Applications
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AI in Heritage Management
Dual Identity of Historic Districts
Methodological Innovation
Leveraging AI for Heritage Conservation
The study demonstrates how advanced AI techniques, including BERTopic for thematic clustering and fine-tuned BERT for sentiment analysis, can effectively process vast amounts of unstructured public feedback. This provides heritage managers with data-informed insights into visitor experiences and resident perceptions, moving beyond traditional, labor-intensive survey methods. The integration of LLMs like DeepSeek V3.2 streamlines the interpretation of complex topic clusters into actionable dimensions, such as 'Hangzhou Urban Tourism & Culture' and 'Qinghefang Culture & Creative', making heritage management more responsive and evidence-based.
Tourist Gaze vs. Living Space
A key finding is the distinct emotional divergence between tourism-oriented and residential-functional dimensions. Tourist experiences of cultural and culinary aspects evoke strong positive emotions, aligning with the 'servicescape' theory where architectural heritage drives satisfaction. In contrast, residential preferences are characterized by neutral sentiment, reflecting a utilitarian evaluation based on price and convenience rather than aesthetic appreciation. This highlights the necessity for differentiated management strategies that cater to both the 'hedonic appreciation' of tourists and the 'functional evaluation' of residents.
Hybrid Human-AI Workflow
The research introduces a novel hybrid human-AI workflow, combining algorithmic clustering (BERTopic) with LLM-based interpretive synthesis (DeepSeek V3.2) and expert verification. This approach ensures that the identified perceptual dimensions are both data-driven and linguistically nuanced, reducing subjective bias inherent in manual annotation. The fine-tuned BERT model achieved high discrimination accuracy (AUC > 0.99) across sentiment categories, enabling precise mapping of emotional responses to specific themes and providing a transparent basis for heritage management decisions.
88.8%
Positive Sentiment for Food & Dining
Enterprise Process Flow
Online Review Collection
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BERTopic Clustering
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LLM Topic Labeling
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BERT Sentiment Analysis
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Actionable Insights
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Qinghefang District: A Dual Ecosystem
The Qinghefang Historical and Cultural District exemplifies the dual identity of historic areas. For tourists, it is a vibrant "servicescape" offering rich cultural experiences, food, and creative industries, leading to overwhelmingly positive sentiment (e.g., 86.9% positive for Urban Tourism). For residents, however, it serves as a "living space" with considerations for housing and daily logistics, resulting in a more neutral sentiment profile (e.g., 37.4% neutral for Housing & Residential Choice). This nuanced understanding, revealed by AI, is crucial for balanced urban planning and sustainable heritage management, ensuring both economic vitality and community well-being.
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