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Voice of airline passenger: A text mining approach to understand customer satisfaction

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  • Sezgen, Eren
  • Mason, Keith J.
  • Mayer, Robert

Abstract

This paper investigates the key drivers of customer satisfaction and dissatisfaction towards both, full-service and low-cost carriers and also towards, economy and premium cabins. Latent Semantic Analysis - a text mining and categorisation technique ─ is applied to analyse online user-generated airline reviews. Over five thousand passenger reviews for fifty (50) airlines were collected from the online review site, TripAdvisor. Findings show that there are fundamental differences in the drivers of passenger satisfaction depending on the class of air travel purchased, and whether the airline is a low cost or a full service carrier. Friendliness and helpfulness of staff are the key factors for those travelling in Economy Class, product value is key for those in premium cabins, and a low price is the key satisfaction driver for those that choose to travel on a low cost airline. The research also shows that the service attributes seat comfort and legroom, luggage/flight disruptions and staff behaviours are the main reasons for passengers’ dissatisfaction among all groups. This study provides an alternative customer satisfaction analysis for managers to hear the voice of their customers by using a well-established text mining technique and by analysing the reviews of satisfied and dissatisfied customers.

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  • Sezgen, Eren & Mason, Keith J. & Mayer, Robert, 2019. "Voice of airline passenger: A text mining approach to understand customer satisfaction," Journal of Air Transport Management, Elsevier, vol. 77(C), pages 65-74.
    • Handle: RePEc:eee:jaitra:v:77:y:2019:i:c:p:65-74
    DOI: 10.1016/j.jairtraman.2019.04.001
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    21. Clempner, Julio B., 2020. "Penalizing passenger’s transfer time in computing airlines revenue," Omega, Elsevier, vol. 97(C).
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