  Air travel is one of the most frequently used means of transportation in our
every-day life. Thus, it is not surprising that an increasing number of
travelers share their experiences with airlines and airports in form of online
reviews on the Web. In this work, we thrive to explain and uncover the features
of airline reviews that contribute most to traveler satisfaction. To that end,
we examine reviews crawled from the Skytrax air travel review portal. Skytrax
provides four review categories to review airports, lounges, airlines and
seats. Each review category consists of several five-star ratings as well as
free-text review content. In this paper, we conducted a comprehensive feature
study and we find that not only five-star rating information such as airport
queuing time and lounge comfort highly correlate with traveler satisfaction but
also textual features in the form of the inferred review text sentiment. Based
on our findings, we created classifiers to predict traveler satisfaction using
the best performing rating features. Our results reveal that given our
methodology, traveler satisfaction can be predicted with high accuracy.
Additionally, we find that training a model on the sentiment of the review text
provides a competitive alternative when no five star rating information is
available. We believe that our work is of interest for researchers in the area
of modeling and predicting user satisfaction based on available review data on
the Web.
