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.
One recent work going into that direction is the one described in @cite_2 , in which the authors mined review data about airlines' in-flight services from the Skytrax portal. By grouping travelers via feature-based and clustering-based modelling, the authors showed that inferences can be captured to explain how travelers evaluate in-flight services. Another recent work of @cite_1 presented a research framework to extract and explore information on a user's opinion about airline service features from a large static corpus of online review texts.
@cite_1 @cite_2
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In this work, we propose a research framework to exploring the useful information about airline service from massive online reviews, especially, the airline service features from the customer perspective. The experimental results indicate that the proposed methods can extract information about customers' opinion about the airline service features. The common concerns as well as special features for different airline company can also be extracted efficiently from the massive online review data. Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous data streams brings unique opportunities, but also new challenges. This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and constant time per example. VFDT can incorporate tens of thousands of examples per second using off-the-shelf hardware. It uses Hoeffding bounds to guarantee that its output is asymptotically nearly identical to that of a conventional learner. We study VFDT's properties and demonstrate its utility through an extensive set of experiments on synthetic data. We apply VFDT to mining the continuous stream of Web access data from the whole University of Washington main campus.