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Using deep learning to investigate digital behavior in culinary tourism

Using deep learning to investigate digital behavior in culinary tourism The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform.Design/methodology/approachUsing 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.FindingsFirst, 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 implicationsThis 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.Originality/valueThis research extends and contributes to the literature regarding visual user-generated content in culinary tourism. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Place Management and Development Emerald Publishing

Using deep learning to investigate digital behavior in culinary tourism

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1753-8335
DOI
10.1108/jpmd-03-2020-0022
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform.Design/methodology/approachUsing 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.FindingsFirst, 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 implicationsThis 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.Originality/valueThis research extends and contributes to the literature regarding visual user-generated content in culinary tourism.

Journal

Journal of Place Management and DevelopmentEmerald Publishing

Published: Jan 14, 2021

Keywords: Deep learning; Text mining; Image analysis; Culinary tourism; Digital consumer behavior; Online contents

References