基于客户评论评估酒店业服务质量的机器学习
Machine learning for assessing quality of service in the hospitality sector based on customer reviews
摘要：
越来越多地使用在线酒店平台提供有关客户偏好的第一手信息，这对于改善酒店服务和提高服务质量感知至关重要。客户评论可以用来自动提取最相关的方面的服务质量的招待客户.本文提出了一个框架的服务质量在酒店业的基础上开发客户评论，通过自然语言处理和机器学习方法的评估。所提出的框架自动发现与酒店客户相关的服务质量方面。Booking.com上来自波哥大和马德里的酒店点评将被自动删除。语义信息是通过潜在的Dirichlet分配和FastText来推断的，它们允许将文本评论表示为向量。降维技术应用于可视化和解释大量的客户评论。生成最重要的服务质量方面的可视化，允许定性和定量地评估服务质量。结果表明，它是可以自动提取的主要服务质量方面的客户感知从大型客户评论数据集。这些研究结果可以被酒店管理者用来更好地了解客户，提高服务的服务质量。
英文摘要：
The increasing use of online hospitality platforms provides firsthand information about clients preferences, which are essential to improve hotel services and increase the quality of service perception. Customer reviews can be used to automatically extract the most relevant aspects of the quality of service for hospitality clientele. This paper proposes a framework for the assessment of the quality of service in the hospitality sector based on the exploitation of customer reviews through natural language processing and machine learning methods. The proposed framework automatically discovers the quality of service aspects relevant to hotel customers. Hotel reviews from Bogotá and Madrid are automatically scrapped from Booking.com. Semantic information is inferred through Latent Dirichlet Allocation and FastText, which allow representing text reviews as vectors. A dimensionality reduction technique is applied to visualise and interpret large amounts of customer reviews. Visualisations of the most important quality of service aspects are generated, allowing to qualitatively and quantitatively assess the quality of service. Results show that it is possible to automatically extract the main quality of service aspects perceived by customers from large customer review datasets. These findings could be used by hospitality managers to understand clients better and to improve the quality of service.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
