使用潜在狄利克雷分配来调查英国COVID-19大流行期间Airbnb住宿的客人体验

Nathakit Keawtoomla, Arinya Pongwat, Jakramate Bootkrajang
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引用次数: 0

摘要

住宿行业的共享经济为旅行者提供了另一种选择,而这一细分市场正在显著增长。为了提供比传统住宿服务更好的服务,特别是在全球旅游业冻结的Covid-19大流行危机期间,了解客人体验的需求非常重要。本研究采用潜在狄利克雷分配技术对Airbnb平台上的评论进行了研究,以了解Airbnb客人在Covid-19危机期间的经历。结果表明,从以往的研究中发现了几个潜在的主题,如住宿、位置、邻里、可达性、便利设施等,并提出了一些独特的主题,可以对现有的知识提出建议。讨论了在旅游业和分析技术背景下的理论和实践贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Latent Dirichlet Allocation to investigate guest experience in Airbnb accommodation during COVID-19 pandemic in the United Kingdom
The sharing economy in the accommodation business provides the alternatives for travelers, while this market segment is growing significantly. It is important to understand the requirement of the guests' experience in order to provide the better service comparing to the traditional accommodation services, especially during the pandemic crisis of Covid-19 when tourism industry was frozen globally. The current study explores the reviews on Airbnb platform by employing the Latent Dirichlet Allocation technique in order to understand the experiences among Airbnb guests during the Covid-19 crisis. The results revealed that several latent topics from previous studies were discovered, such as accommodation, location, neighborhood, accessibility, amenities, etc., with some unique topics that can be suggested to the existing knowledge. The theoretical and practical contributions in both tourism and the analysis technique contexts were discussed.
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