The tourism and hospitality sectors contribute significantly to the Indonesian economy. Meanwhile, COVID-19 affects these sectors. During the pandemic, the Indonesian government applied quarantine regulations at designated hotels to support its tourism industry. Since COVID-19 is becoming endemic and travel bans are being relaxed, hotel satisfaction becomes a crucial factor in quarantine hotels. If guests have a positive experience while staying at these hotels, they are likely to return for a staycation or vacation in the near future. The study examined 4856 reviews from Google reviews on 15 quarantine hotels in Indonesia. Following word frequency calculations in a matrix, UCINET 6.0 is used to analyze the network centrality and perform CONCOR analysis. The CONCOR analysis categorizes the review data into five categories. As quantitative analysis was performed, exploratory factor analysis was grouped into six variables: tangible, assurance, frontline, accommodation, quarantine, and location. As a result, tangible, assurance, and frontline negatively impacted guest satisfaction. Furthermore, three other variables: accommodation, quarantine, location, which have a positive influence, will lead to increased trust from inbound travelers. For managerial implication, results allow managers of quarantine hotels in Indonesia to focus more on improving tangible, assurance, and frontline factors.

Singapore relies heavily on the tourism industry, which was severely affected by COVID-19. During the early phase of the pandemic, the Singapore government created a campaign reassuring locals and encouraging them to “travel” within Singapore. During the pandemic, travelers’ focus shifted to pandemic-related topics. This study examined 8441 customer textual reviews from seven luxury hotels in the Marina Bay area through Google Travel derived from SCTM 3.0. In order to determine the new attributes affecting customer satisfaction, this study used UCINET 6.0 and Text2Data as part of text mining. Subsequently, SPSS was used for descriptive analysis and regression analysis to identify the relationship between the attributes in the customer textual reviews and the overall satisfaction of the customers. The results showed that all the attributes were significant in terms of overall customer satisfaction, with three attributes, sentiment polarity, readability, and word length, positively affecting overall customer satisfaction. Through social media and online platforms, consumers express their thoughts and feelings about online reviews of many products and services. With the adopted methodology, the industry may be able to benefit from this abundance of information in order to adjust strategies and increase financial benefits post-COVID-19.

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

The purpose of this study is to examine customer satisfaction of halal restaurants that have received halal certification from the Korea Muslim Federation (KMF). A total of 1,544 reviews from 11 halal-certified restaurants in South Korea were collected from Google reviews using SCTM 3.0 (Smart Crawling and Text Mining). UCINET 6.0 is used to analyze the network centrality and perform CONCOR analysis. This study quantitatively identified the relationship among five dimensions: halal cuisine, Islamic dining, food menu, service and value. However, Islamic dining, service, and value were positively impacted to customer satisfaction. This study indicates that the Halal label is a significant factor in the dining preferences of halal restaurant customers in South Korea. The results of this study may be useful in marketing halal restaurants in South Korea which have not applied for halal certification from the Korea Muslim Federation.

By exploring the top three airports in Asia, this study explores the area of smart airport systems. With the goal of analyzing the significant elements of airport services that captivate travelers’ attention through online reviews and establishing a correlation between sentiment in reviews and numerical ratings given by travelers, the study analyzes what captivates travelers’ attention. Data mining, frequency analysis, sentiment analysis, and linear regression are employed in this study in order to analyze a dataset of 10,202 online reviews. The results indicate that the most common attributes of airport services significantly impact customer satisfaction, as well as how the sentiment expressed in online reviews correlates with the numerical ratings. A significant contribution of this study lies in its contribution to understanding the dynamics of customer satisfaction in the field of airport services as well as in identifying areas for improvement that could enhance the overall traveler experience in the burgeoning field of smart airports. In the context of smart airport systems, the analysis of exploratory and predictive data provides valuable insights into the optimization of airport operations, thus enriching the body of knowledge in this rapidly evolving area and providing the foundation for future research.

The tourism industry plays a significant role in the economy of Bangladesh, particularly in the world-renowned coastal town of Cox’s Bazar. However, the COVID-19 pandemic has significantly disrupted this sector, leading to substantial economic losses and shifts in customer satisfaction and behavior. This study aims to analyze the change in customer satisfaction in Cox’s Bazar hotels and resorts before and after the pandemic. In order to identify user-generated content from 9481 reviews from 11 hotels, Linguistic Inquiry and Word Count (LIWC-22) software was used for text analysis, followed by factor analysis and regression analysis. The study highlights the increased importance of “Financial Stability” post-pandemic, likely due to price reductions and special offers. Additionally, “Digital Culture” and “Illness” emerged as new dissatisfaction factors. The insights offer valuable implications for businesses, policymakers, and tourism stakeholders to strategize effective customer service and foster sustainable recovery in the post-pandemic era.