| Summary: | This study analyzes and extracts the TripAdvisor customers’ experiences from the user-generated data as the topic of interest from online review customers in TripAdvisor application. The aims of this study were to illustrate on how customers’ reviews on social media are able to be used as an evaluation and visual modeling tool. The online review customers are processed using sentiment analysis and text mining. Findings show that customers often review their experience where they have stayed based on their last stay period. The Decision Tree Algorithm is better to classify the sentiment analysis result than the Naïve Bayes Algorithm in the field of accuracy. However, in the field of precision and recall, Naïve Bayes Algorithm is often better than the Decision Tree Algorithm. The text mining results reveal that TripAdvisor customers tend to use words such as “night”, “pool”, and “time” in negative sentiments expressed after or during a stay.
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