Online communities are identified as people gathering online and communicating through the internet to share ideas, objectives, goals, without any geographical boundary. The growth of user-generated content created in online communities has transformed the way consumers search for and share information, particularly in the hospitality industry. Particularly, in the restaurant and food sectors due to the intangible nature of hospitality services, online reviews play an important role on consumer decisions. Furthermore, online reviews on restaurants are not only informational but also, they impact consumers’ choices regarding restaurants. Consequently, the nature of such user-generated content that is produced at a high speed and is diverse and rich should be treated and understood. This study proposes the first tailored BERTopic model together with sentiment analysis based on pre-trained BERT model that takes advantage of its novel sentence embedding for creating interpretable topics into the analysis of restaurant online reviews to determine how the customers elaborate their criteria in the context of certain experiences. An exploratory analysis is presented involving a large-scale review data set of 261,531 restaurant online reviews from 4 different countries retrieved from the eWOM community thefork.com. A broad list of the topics discussed by customers post-dining in restaurants is built. Insights into the behavior, experience, and satisfaction of the customers across the different restaurants are discovered. This approach and findings are encouraging hospitality managers in understanding customers’ perception, through which applicable marketing can be developed to attract and retain potential customers.