Exploring destination image of dark tourism via analyzing user generated photos: A deep learning approach

Qian, Lili; Guo, Juncheng; Qiu, Hanqin; Zheng, Chunhui; Ren, Lianping

doi:10.1016/j.tmp.2023.101147

Cited by 11 publications

(2 citation statements)

References 81 publications

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“…Arefieva et al [79] utilized the power of deep learning to cluster Instagram images, providing insights into the visual representations of tourist destinations. Qian et al [80] analyzed user-generated photos to explore the destination images of dark tourism.…”

Section: Advanced Learning On Destination Image Perceptionmentioning

confidence: 99%

“…This approach is particularly impactful in tourism, where network analysis illuminates the connections between destinations, enhancing tourism quality [28]. It effectively maps tourist mobility, uncovering the spatial patterns and relationships among destinations [80]. Studies [29,88,89] have demonstrated its application in analyzing tourist flows and networks globally.…”

Section: Network Science In Tourismmentioning

confidence: 99%

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Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility

Ramadhani,

Alamsyah,

Febrianta

et al. 2024

JTAER

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The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.

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Section: Advanced Learning On Destination Image Perceptionmentioning

confidence: 99%

Section: Network Science In Tourismmentioning

confidence: 99%

Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility

Ramadhani,

Alamsyah,

Febrianta

et al. 2024

JTAER

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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Exploring the impact of seasonal forest landscapes on tourist emotions using Machine learning

Chen,

Ye,

Yang

et al. 2024

Ecological Indicators

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Exploring destination image of dark tourism via analyzing user generated photos: A deep learning approach

Cited by 11 publications

References 81 publications

Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility

Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility

Machine learning applied to tourism: A systematic review

Exploring the impact of seasonal forest landscapes on tourist emotions using Machine learning

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