Using geotagged facial expressions to visualize and characterize different demographic groups’ emotion in theme parks

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists' emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups' emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups' emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists' emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

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Publication Details

Journal
Scientific Reports
Published
2024-09-09
DOI
https://doi.org/10.1038/s41598-024-69555-5
Citations
1
Primary Topic
Consumer Behavior in Brand Consumption and Identification
Type
article
Field-Weighted Citation Impact
0.44

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article

Using geotagged facial expressions to visualize and characterize different demographic groups’ emotion in theme parks

Wei Jiang, Xinyu Xia, Junjun Zhi, Haoze Wu et al.
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Scientific Reports
Consumer Behavior in Brand Consumption and Identification
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article

Using geotagged facial expressions to visualize and characterize different demographic groups’ emotion in theme parks

Wei Jiang, Xinyu Xia, Junjun Zhi, Haoze Wu, Yi Long, Xiaoqing Song, Qin Su
article en
1 citations

Abstract

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists' emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups' emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups' emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists' emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

Scientific ReportsVol. 14(1)
Nanjing Normal University (CN), Wuhu Institute of Technology (CN), Anhui Normal University (CN)
National Natural Science Foundation of China, Ministry of Education, India, Natural Science Foundation of Anhui Province
Decent work and economic growth
Openalex Percentile: Top 28%
Consumer Behavior in Brand Consumption and Identification
0.44
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