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Using deep learning to investigate digital behavior in culinary tourism

Md Rajibul Hasan (School of Business, Maynooth University, Maynooth, Ireland)
Assem Abdunurova (Almaty Management University, Almaty, Kazakhstan)
Wenwen Wang (Rennes School of Business, Rennes, France)
Jiawei Zheng (Rennes School of Business, Rennes, France)
S.M. Riad Shams (Newcastle University Business School, Newcastle upon Tyne, UK)

Journal of Place Management and Development

ISSN: 1753-8335

Article publication date: 3 November 2020

Issue publication date: 14 January 2021




The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform.


Using the deep learning approach, this research assessed consumer-posted online content of dining experiences by implementing image analysis and clustering. Text mining using word cloud analysis revealed the most frequently repeated keywords.


First, 4,000 photos of nine Chinese restaurants posted on Tripadvisor’s website were analyzed using image recognition via Inception V3 and Google’s deep learning network; this revealed 12 hierarchical image clusters. Then, an open-questionnaire survey of 125 Chinese respondents investigated consumers’ information needs before visiting a restaurant and after purchasing behavior (motives to share).

Practical implications

This study contributes to culinary marketing development by introducing a new analysis methodology and demonstrating its application by exploring a wide range of keywords and visual images published on the internet.


This research extends and contributes to the literature regarding visual user-generated content in culinary tourism.



Hasan, M.R., Abdunurova, A., Wang, W., Zheng, J. and Shams, S.M.R. (2021), "Using deep learning to investigate digital behavior in culinary tourism", Journal of Place Management and Development, Vol. 14 No. 1, pp. 43-65.



Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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