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Digital social media has played a key role in tourism and hospitality industry. The use of machine and deep learning has been effective in market segmentation and customers' preference prediction through social big data analysis. This paper develops a new method to analyze large set of open data in social networking sites for travellers segmentation and predict tourists' choice preferences using dimensionality reduction and deep learning techniques. Deep belief network was used for predicting the travellers' choice preferences from their past ratings and online reviews. Self-organizing map was also used for clustering the travellers' online ratings and reviews. The feature extraction is performed using latent Dirichlet allocation as an unsupervised learning technique. To improve the effectiveness of learning, a dimensionality reduction technique, higher-order singular value decomposition, is performed on the clusters for the prediction of missing values and traveller–traveller similarity calculation. The proposed method was evaluated on travellers' online reviews and ratings which were crawled from TripAdvisor. The results showed the robustness of the proposed method in analysing the large text-based reviews and numerical datasets in tourism context. [ABSTRACT FROM AUTHOR]

• This study investigates the impact of service quality on customers' satisfaction during COVID-19. • The use of text mining to discover satisfaction dimensions. • A new method for online customers' reviews analysis is proposed. • New findings are presented on the impact of service quality on customers' satisfaction during COVID-19. • Evaluation of method through the data in TripAdvisor. 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. [ABSTRACT FROM AUTHOR]