Exploring visitor perception of Asian historic districts through deep learning and social media data - Yinying Tao; Yifeng Jiang; Manshu Huang; Kai Zhou (Journal of Asian Architecture and Building Engineering)
The preservation and management of historic and cultural districts face significant challenges due to urbanization and commercialization. This study utilizes advanced deep learning techniques and sentiment analysis to assess visitor perceptions of the Xiaohe Straight Street Cultural and Historical District in Hangzhou, a site undergoing efforts of preservation and revitalization. Employing the BERT-BiLSTM-Attention model and analyzing social media data from Weibo and Dianping, this research captures the nuanced emotional responses of visitors, offering actionable insights for cultural heritage management. Additionally, Importance-Performance Analysis (IPA) is applied to identify key factors influencing visitor satisfaction and areas for improvement. The IPA framework highlights the importance of the district’s architecture, cultural heritage preservation, and accessibility, while areas such as commercial influence, transportation, and overcrowding require attention. Sentiment analysis revealed a positive perception of the district’s architectural and cultural elements, but dissatisfaction with commercial pressures and accessibility issues. The findings emphasize the need for a balanced approach between tourism development and historical authenticity preservation. This interdisciplinary approach contributes valuable insights to heritage management, demonstrating the effectiveness of combining AI-driven techniques with visitor feedback to enhance the sustainability of cultural heritage sites.