The rapid advancement of digital technology has significantly transformed the tourism industry, particularly through peer-to-peer (P2P) platforms like Airbnb, which offer personalised travel experiences. In this evolving landscape, it becomes increasingly important to analyse the sentiments conveyed in user-generated content (UGC), as they provide valuable insights into customer perceptions and preferences. This study examines the sentiments expressed by tourists in reviews of Airbnb experiences, across four major Spanish cities (Barcelona, Madrid, Valencia, and Seville) and global online experiences. The research employs sentiment analysis using the Vader method, integrated with compositional data analysis (CoDa), to quantify and compare the emotional content of these reviews. A total of 182,743 reviews were analysed across 385 experiences. The findings reveal that online and in-person experiences generate different balances of positive and negative sentiments, with categories such as cooking and art & culture eliciting higher positive sentiment. Additionally, a correlation was found between higher ratings and positive sentiments, while higher prices and longer durations were associated with more negative sentiments. These insights are crucial for optimizing experience offerings and improving customer satisfaction on platforms like Airbnb.