Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews
Online reviews are crucial for identifying the factors that affect the dynamic evolution of tourist sentiment, improving tourist satisfaction. This study employs pre-trained models BERT and BERTopic and social network analysis to examine 228,062 reviews collected from Ctrip using Python. The factors...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/20/1/22 |
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| _version_ | 1849341939748438016 |
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| author | Bingbing Wang Qiuhao Zhao Zhe Zhang Pengfei Xu Xin Tian Pingbin Jin |
| author_facet | Bingbing Wang Qiuhao Zhao Zhe Zhang Pengfei Xu Xin Tian Pingbin Jin |
| author_sort | Bingbing Wang |
| collection | DOAJ |
| description | Online reviews are crucial for identifying the factors that affect the dynamic evolution of tourist sentiment, improving tourist satisfaction. This study employs pre-trained models BERT and BERTopic and social network analysis to examine 228,062 reviews collected from Ctrip using Python. The factors influencing tourist sentiment across natural tourism attractions (NTAs), cultural tourism attractions (CTAs), and theme park tourism attractions (TPTAs) were explored before, during, and after the pandemic. The findings reveal that there was minimal change in the types of factors influencing before and during the pandemic, significant changes in the values of factors during the pandemic, and fluctuations in both the types and values of factors after the pandemic. Regardless of the period, influences on negative sentiment were more dispersed, while positive emotions were more polarized. Based on these insights, we propose theoretical contributions and improvement strategies for enhancing resilience and promoting high-quality development in different types of attractions. |
| format | Article |
| id | doaj-art-d5057f4d0e3c4124adef4a500044307b |
| institution | Kabale University |
| issn | 0718-1876 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj-art-d5057f4d0e3c4124adef4a500044307b2025-08-20T03:43:31ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762025-02-012012210.3390/jtaer20010022Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online ReviewsBingbing Wang0Qiuhao Zhao1Zhe Zhang2Pengfei Xu3Xin Tian4Pingbin Jin5School of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310013, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310058, ChinaOnline reviews are crucial for identifying the factors that affect the dynamic evolution of tourist sentiment, improving tourist satisfaction. This study employs pre-trained models BERT and BERTopic and social network analysis to examine 228,062 reviews collected from Ctrip using Python. The factors influencing tourist sentiment across natural tourism attractions (NTAs), cultural tourism attractions (CTAs), and theme park tourism attractions (TPTAs) were explored before, during, and after the pandemic. The findings reveal that there was minimal change in the types of factors influencing before and during the pandemic, significant changes in the values of factors during the pandemic, and fluctuations in both the types and values of factors after the pandemic. Regardless of the period, influences on negative sentiment were more dispersed, while positive emotions were more polarized. Based on these insights, we propose theoretical contributions and improvement strategies for enhancing resilience and promoting high-quality development in different types of attractions.https://www.mdpi.com/0718-1876/20/1/22tourist sentimentinfluencing factorstourist attractionsheterogeneitydynamicstopic mining |
| spellingShingle | Bingbing Wang Qiuhao Zhao Zhe Zhang Pengfei Xu Xin Tian Pingbin Jin Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews Journal of Theoretical and Applied Electronic Commerce Research tourist sentiment influencing factors tourist attractions heterogeneity dynamics topic mining |
| title | Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews |
| title_full | Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews |
| title_fullStr | Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews |
| title_full_unstemmed | Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews |
| title_short | Understanding the Heterogeneity and Dynamics of Factors Influencing Tourist Sentiment with Online Reviews |
| title_sort | understanding the heterogeneity and dynamics of factors influencing tourist sentiment with online reviews |
| topic | tourist sentiment influencing factors tourist attractions heterogeneity dynamics topic mining |
| url | https://www.mdpi.com/0718-1876/20/1/22 |
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