The tourism industry significantly influences the global economy. Along with technological advancements, digital reviews have become crucial in guiding tourists to their preferred destinations. However, the inherently textual nature of reviews presents challenges for traditional processing methods, necessitating specialized approaches. This research leverages Natural Language Processing (NLP) to analyze tourist reviews for popular destinations in Indonesia and Thailand. The sentiment analysis identified high tourist satisfaction levels across destinations, quantifying at 77.18 for Indonesia and 78.52 for Thailand. Furthermore, we applied multiclass text classification methodologies to identify the primary dimensions of perceived tourist experiences. Entertainment emerged as the predominant reason for tourists visiting destinations in Indonesia and Thailand. Each country offering distinct favored entertainment experiences; nature-based entertainment in Indonesia, whereas nightlife and festive entertainment in Thailand. This study provides valuable insights for government authorities and tourism stakeholders on the perception of tourist experiences, supporting and enhancing destination appeal within both countries.