Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development
With the growth of tourism and social media, understanding visitor perceptions of destinations has become essential for tourism management and planning. Built heritage, as a cultural asset and tourist attraction, plays a significant role in sustainable urban development, but there is a research gap...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2540079 |
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| author | Hao Yuan Rui Ke Xubin Xie |
| author_facet | Hao Yuan Rui Ke Xubin Xie |
| author_sort | Hao Yuan |
| collection | DOAJ |
| description | With the growth of tourism and social media, understanding visitor perceptions of destinations has become essential for tourism management and planning. Built heritage, as a cultural asset and tourist attraction, plays a significant role in sustainable urban development, but there is a research gap in exploring visitor perceptions of historic districts, particularly through fine-grained sentiment analysis using social media data. Fine-grained sentiment analysis, which examines both sentiment polarity and intensity, allows for a deeper understanding of visitor experiences by capturing the nuanced emotional responses of visitors to specific aspects of the destination. Conventional survey-based approaches often fail to capture the complexity and real-time nuances of visitor sentiments, necessitating advanced AI-driven methodologies. This study introduces a cascaded deep learning framework that first identifies key aspects of visitor experiences and then classifies their sentiment polarity and intensity. The results demonstrate the model’s effectiveness in providing detailed insights into visitors’ perceptions and satisfaction, offering data-driven recommendations for the conservation and optimization of historic districts. The innovation of this study lies in its integration of multi-task learning for fine-grained sentiment analysis, contributing valuable insights for heritage tourism management. |
| format | Article |
| id | doaj-art-21b2dff6a6134efea706b3b996a1f5ba |
| institution | DOAJ |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-21b2dff6a6134efea706b3b996a1f5ba2025-08-20T02:57:07ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-08-010011610.1080/13467581.2025.25400792540079Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and developmentHao Yuan0Rui Ke1Xubin Xie2Central South UniversityCentral South UniversityCentral South UniversityWith the growth of tourism and social media, understanding visitor perceptions of destinations has become essential for tourism management and planning. Built heritage, as a cultural asset and tourist attraction, plays a significant role in sustainable urban development, but there is a research gap in exploring visitor perceptions of historic districts, particularly through fine-grained sentiment analysis using social media data. Fine-grained sentiment analysis, which examines both sentiment polarity and intensity, allows for a deeper understanding of visitor experiences by capturing the nuanced emotional responses of visitors to specific aspects of the destination. Conventional survey-based approaches often fail to capture the complexity and real-time nuances of visitor sentiments, necessitating advanced AI-driven methodologies. This study introduces a cascaded deep learning framework that first identifies key aspects of visitor experiences and then classifies their sentiment polarity and intensity. The results demonstrate the model’s effectiveness in providing detailed insights into visitors’ perceptions and satisfaction, offering data-driven recommendations for the conservation and optimization of historic districts. The innovation of this study lies in its integration of multi-task learning for fine-grained sentiment analysis, contributing valuable insights for heritage tourism management.http://dx.doi.org/10.1080/13467581.2025.2540079tourist perceptionsarchitectural heritagephoenix ancient townhistoric districtsheritage conservation |
| spellingShingle | Hao Yuan Rui Ke Xubin Xie Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development Journal of Asian Architecture and Building Engineering tourist perceptions architectural heritage phoenix ancient town historic districts heritage conservation |
| title | Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development |
| title_full | Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development |
| title_fullStr | Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development |
| title_full_unstemmed | Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development |
| title_short | Sentiment analysis of visitor perceptions on architectural heritage: a case study of Phoenix Ancient Town for sustainable conservation and development |
| title_sort | sentiment analysis of visitor perceptions on architectural heritage a case study of phoenix ancient town for sustainable conservation and development |
| topic | tourist perceptions architectural heritage phoenix ancient town historic districts heritage conservation |
| url | http://dx.doi.org/10.1080/13467581.2025.2540079 |
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