High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

TL;DR

This study analyzes airline reviews from Skytrax to identify key features influencing traveler satisfaction, demonstrating that both ratings and review sentiment can effectively predict satisfaction levels.

Contribution

The paper provides a comprehensive feature analysis of airline reviews and develops classifiers that accurately predict traveler satisfaction using ratings and textual sentiment.

Findings

Rating features like queuing time and lounge comfort correlate with satisfaction

Text sentiment analysis can predict satisfaction when rating data is unavailable

Classifiers achieve high accuracy in satisfaction prediction

Abstract

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.