Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach
Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach
复制标题
人满为患的景点的旅游经验：一种文本分析方法
DOI：
10.1007/978-3-030-65785-7_21
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发表时间：
2020-11-28
期刊：
影响因子：
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通讯作者：
Egger R
中科院分区：
文献类型：
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作者：
Yu J;Egger R
由于旅游活动，过度旅游已成为一个全球性问题。即使在COVID-19大流行之后，过度旅游的话题将有利于局部过度拥挤，这是旅游业的一个新现象。由于没有具体的测量方法来评估游客在拥挤景点的体验，本研究旨在通过主题建模和情感分析，基于截至2019年底的TripAdvisor在线评论，探索游客在访问热门和拥挤景点时的感知和感受。通过调查巴黎的10大景点，结果呈现了游客经常讨论的24个话题。与过度旅游有关的一些主题的例子是安全，服务，排队和社会互动。具体而言，在所有确定的主题中，游客对安全和安保的感受最消极。通过桥接过度旅游，文本分析和用户生成的内容，这项研究有助于旅游体验和人群管理领域。
As a result of travel activities, overtourism has become a global issue. Even after the COVID-19 pandemic, the topic of overtourism would benefit localized overcrowding as a new occurrence in the tourism industry. Since there is no specific measurement to evaluate tourist experiences at crowded attractions, this study aims to explore the perception and feelings of tourists when they visit popular and crowded attractions through topic modeling and sentiment analysis based on TripAdvisor online reviews as of the end of 2019. By investigating the top 10 attractions in Paris, the results present 24 topics frequently discussed by tourists. Examples of some topics related to overtourism are safety, service, queuing, and social interaction. Specifically, tourists felt the most negative towards safety and security among all the identified topics. By bridging overtourism, text analytics, and user-generated-content, this study contributes to the field of tourist experiences and crowd management.