Authors - Yisel Clavel-Quintero, Ernesto Gongora-Rodriguez, Melissa Carmenaty-Ramirez Abstract - The Internet has signicantly transformed the business landscape, particularly in the tourism industry, by removing geographical constraints and time restrictions, while enhancing accessibility for consumers. Nowadays, users tend to search online for destinations and opinions from other travelers, make reservations, and share their own assessments. Therefore, customer reviews have become a valuable source of information for companies seeking to evaluate service quality and improve their products, advertising strategies, and overall performance. In this context, opinion mining and sentiment analysis have gained relevance, particularly in platforms such as TripAdvisor, which rely on usergenerated content. A key challenge in polarity detection is the correct interpretation of irony, as it can alter the intended meaning and sentiment of an expression. However, there are still few available TripAdvisor datasets, and, to the best of our knowledge, none are labeled for irony. We propose the creation of a dataset of TripAdvisor reviews annotated with both polarity and irony, alongside an experimental study to identify a model capable of eectively classifying the polarity of ironic TripAdvisor user reviews. Transfer learning was applied by adapting models trained on two source datasets for irony detection, and the best-performing model was subsequently used to annotate a TripAdvisor dataset with irony. Furthermore, experiments for polarity classication were conducted. The logistic regression model achieved the best performance in both tasks. The dataset obtained oers a valuable resource for future research on sentiment analysis and opinion mining in the tourism domain.
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