Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system.
Perceptual recognition of tourist destinations is vital in representing the destination image, supporting destination management decision-making, and promoting tourism recommendations. However, previous studies on tourist destination perception have limitations regarding accuracy and completeness re...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318846 |
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| _version_ | 1823856831810764800 |
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| author | Shengtian Zhang Yong Li Xiaoxia Song Chenghao Yang Niusha Shafiabady Robert M X Wu |
| author_facet | Shengtian Zhang Yong Li Xiaoxia Song Chenghao Yang Niusha Shafiabady Robert M X Wu |
| author_sort | Shengtian Zhang |
| collection | DOAJ |
| description | Perceptual recognition of tourist destinations is vital in representing the destination image, supporting destination management decision-making, and promoting tourism recommendations. However, previous studies on tourist destination perception have limitations regarding accuracy and completeness related to research methods. This study addresses these limitations by proposing an efficient strategy to achieve precise perceptual recognition of tourist destinations while ensuring the integrity of user-generated content (UGC) data and the completeness of perception dimensions. We integrated various types of UGC data, including images, texts, and spatiotemporal information, to create a comprehensive UGC dataset. Then, we adopted the improved Inception V3 model, the bidirectional long short-term memory network (BiLSTM) model with multi-head attention, and geographic information system (GIS) technology to recognize basic tourist feature information from the UGC dataset, such as the content, sentiment, and spatiotemporal perceptual dimensions of the data, achieving a recognition accuracy of over 97%. Finally, a progressive dimension combination method was proposed to visualize and analyze multiple perceptions. An experimental case study demonstrated the strategy's effectiveness, focusing on tourists' perceptions of Datong, China. Experimental results show that the approach is feasible for studying tourist destination perception. Content perception, sentiment perception, and the perception of Datong's spatial and temporal characteristics were recognized and analyzed efficiently. This study offers valuable guidance and a reference framework for selecting methods and technical routes in tourist destination perception. |
| format | Article |
| id | doaj-art-72714d62a09d4d9186ec9705c0588f25 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-72714d62a09d4d9186ec9705c0588f252025-02-12T05:31:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031884610.1371/journal.pone.0318846Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system.Shengtian ZhangYong LiXiaoxia SongChenghao YangNiusha ShafiabadyRobert M X WuPerceptual recognition of tourist destinations is vital in representing the destination image, supporting destination management decision-making, and promoting tourism recommendations. However, previous studies on tourist destination perception have limitations regarding accuracy and completeness related to research methods. This study addresses these limitations by proposing an efficient strategy to achieve precise perceptual recognition of tourist destinations while ensuring the integrity of user-generated content (UGC) data and the completeness of perception dimensions. We integrated various types of UGC data, including images, texts, and spatiotemporal information, to create a comprehensive UGC dataset. Then, we adopted the improved Inception V3 model, the bidirectional long short-term memory network (BiLSTM) model with multi-head attention, and geographic information system (GIS) technology to recognize basic tourist feature information from the UGC dataset, such as the content, sentiment, and spatiotemporal perceptual dimensions of the data, achieving a recognition accuracy of over 97%. Finally, a progressive dimension combination method was proposed to visualize and analyze multiple perceptions. An experimental case study demonstrated the strategy's effectiveness, focusing on tourists' perceptions of Datong, China. Experimental results show that the approach is feasible for studying tourist destination perception. Content perception, sentiment perception, and the perception of Datong's spatial and temporal characteristics were recognized and analyzed efficiently. This study offers valuable guidance and a reference framework for selecting methods and technical routes in tourist destination perception.https://doi.org/10.1371/journal.pone.0318846 |
| spellingShingle | Shengtian Zhang Yong Li Xiaoxia Song Chenghao Yang Niusha Shafiabady Robert M X Wu Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. PLoS ONE |
| title | Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. |
| title_full | Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. |
| title_fullStr | Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. |
| title_full_unstemmed | Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. |
| title_short | Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system. |
| title_sort | multi dimensional perceptual recognition of tourist destination using deep learning model and geographic information system |
| url | https://doi.org/10.1371/journal.pone.0318846 |
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