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.