Airbnb Experiences is a new type of service launched by Airbnb in November 2016 where users can offer travellers a wide range of activities. This study devotes attention to analysing customer feedback expressed in online reviews published in Airbnb to evaluate those experiences.
A total of 1,110 reviews were collected from twelve categories, including 111 experiences, thus ten reviews per experience. First, the sentiment score was computed based on the text of the reviews. Second, seventeen quantitative features encompassing user, experience, and review information were used to model the score through a support vector machine. Third, a sensitivity analysis was performed to extract knowledge on the most relevant features influencing the sentiment score.
Touristswriting online reviews are not only influenced by their tourist experience, but also by their own online experience with the booking and online review platform. The number of reviews made by the user accounted for more than 20% of relevance, while users with more reviews tend to grant more positive reviews.
Current literature is enhanced with a conceptual model grounded on existing studies that assess tourist satisfaction with tour services. Both services online visibility and user characteristics have shown significant importance to tourist satisfaction, adding to the existing body of knowledge.