Analysing the causes of tourists' emotional experience related to tourist attractions from a binary emotions perspective utilising machine learning models

This study constructs a theoretical framework to analyse the causes of tourists' binary emotional experiences. It applies Support Vector Machine (SVM) and Latent Dirichlet allocation (LDA) machine learning models, combined with geospatial analysis methods, to online reviews of five types of tou...

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Bibliographic Details
Published in:Asia Pacific journal of tourism research 2024-06, Vol.29 (6), p.699-718
Main Authors: Yin, Xiaoyan, Jung, Taeyeol
Format: Article
Language:English
Subjects:
Emotional experience
Latent Dirichlet allocation
online reviews
sentiment analysis
Support Vector Machine
topic modelling
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Summary:This study constructs a theoretical framework to analyse the causes of tourists' binary emotional experiences. It applies Support Vector Machine (SVM) and Latent Dirichlet allocation (LDA) machine learning models, combined with geospatial analysis methods, to online reviews of five types of tourist attractions in Dali, China. The results indicate that positive sentiments predominated across Dali Prefecture, though some attractions in Dali City received negative ratings. Furthermore, service experience and price were common influences on tourists' sentiments. This study reveals the causes of tourists' varied emotional experiences at tourist attractions from a binary emotional perspective.
ISSN:1094-1665
1741-6507
DOI:10.1080/10941665.2024.2343077