The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers’ satisfaction through survey-based and online customers’ reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers’ reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers’ satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers’ reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers’ concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers’ reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.

COVID-19 大流行在医疗保健和经济领域引起了全球重大变化。这次大流行导致了消费者习惯和行为的重大变化，这主要是由于与限制有关的条件。尽管已经有几项研究通过基于调查和在线客户评论来分析客户满意度，但迄今为止尚未调查COVID-19对客户满意度的影响。重要的是要调查在线客户评论的满意度维度，以揭示他们在 COVID-19 爆发期间对酒店服务的偏好。本研究旨在通过在线客户评论来揭示新型冠状病毒肺炎 (COVID-19) 爆发期间旅客对马来西亚酒店的满意度。此外，本研究还调查了 COVID-19 期间的服务质量是否对酒店绩效标准以及客户满意度产生影响。因此，我们通过机器学习方法开发了一种新方法。该方法是使用文本挖掘、聚类和预测学习技术开发的。我们使用潜在狄利克雷分配 (LDA) 进行大数据分析，以识别客户的声音，使用期望最大化 (EM) 进行聚类，使用 AFIS 进行满意度预测。此外，我们使用高阶奇异值分解（HOSVD）进行缺失值插补。这些数据是从 TripAdvisor 收集的，以关于新型冠状病毒 (COVID-19) 疫情的在线评论和不同角度对酒店服务的数字评分的形式呈现旅行者关心的问题。对在线客户评论的分析结果表明，COVID-19 期间的服务质量会影响酒店绩效标准，进而影响客户满意度。 此外，结果显示，尽管顾客总是在寻找性能更好的酒店，但他们也担心在COVID-19疫情期间相关服务的质量。