Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system.

Zhang, S; Li, Y; Song, X; Yang, C; Shafiabady, N; Wu, RMX

Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system.

Zhang, S Li, Y Song, X Yang, C Shafiabady, N Wu, RMX

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PLoS One, 2025, 20, (2), pp. e0318846
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Zhang, S
dc.contributor.author	Li, Y
dc.contributor.author	Song, X
dc.contributor.author	Yang, C
dc.contributor.author	Shafiabady, N
dc.contributor.author	Wu, RMX
dc.contributor.editor	ÇALLI, L
dc.date.accessioned	2025-02-25T02:34:33Z
dc.date.available	2024-12-10
dc.date.available	2025-02-25T02:34:33Z
dc.date.issued	2025
dc.identifier.citation	PLoS One, 2025, 20, (2), pp. e0318846
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/185292
dc.description.abstract	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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0318846
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Deep Learning
dc.subject.mesh	Geographic Information Systems
dc.subject.mesh	Humans
dc.subject.mesh	Tourism
dc.subject.mesh	China
dc.subject.mesh	Perception
dc.subject.mesh	Humans
dc.subject.mesh	Perception
dc.subject.mesh	Geographic Information Systems
dc.subject.mesh	China
dc.subject.mesh	Deep Learning
dc.subject.mesh	Tourism
dc.subject.mesh	Deep Learning
dc.subject.mesh	Geographic Information Systems
dc.subject.mesh	Humans
dc.subject.mesh	Tourism
dc.subject.mesh	China
dc.subject.mesh	Perception
dc.title	Multi-dimensional perceptual recognition of tourist destination using deep learning model and geographic information system.
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-02-25T02:34:30Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	20
utslib.citation.issue	2

Abstract:

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.

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http://hdl.handle.net/10453/185292