An experimental study for identifying customer prominent viewpoints on different flight classes by topic modeling methods

Abstract

The airline industry has a wide range of customers with different tastes and expectations of airline services. Therefore, knowing the different attitudes of customers and consequently addressing their opinions is paramount in this business. Airlines offer different flight classes to customers to provide passenger transportation services and use different methods to evaluate customer satisfaction. One of the methods used to analyze customer opinions is the use of text analysis with machine learning techniques. In this study, to analyze the textual data, we used topic modeling to identify passengers' attitudes. In the first stage, we evaluated the performance of three different topic modeling methods: Latent Semantic Indexing (LSI), Hierarchical Dirichlet Process (HDP), and Latent Dirichlet Allocation (LDA). As a result of the modeling process, LDA had the best performance with an average coherence value of 0.42. Eventually, after determining the optimum number of topics as 10 topics, the prominent topics were interpreted and explained. The results of this study demonstrate the common and distinct attitudes of customers of economy class, business class, and first class. As appeared in the results of the economy flight class, the most important topic that appeared in comments was ''passenger care''. However, in whole reviews and reviews of business and first flight classes, the topic of ''time'' was more repetitive. Given the wide application of the presented methodology, the findings of this research can provide airline managers with a broader insight into passenger preferences and guide them on how to provide quality service for their customers.

Keywords

• Topic modeling
• Airline passenger reviews
• Flight classes
• Text mining
• Latent Dirichlet Allocation