Understanding Customers' Evaluations Through Mining Airline Reviews

Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn customer expectations and requirements. Airline customers have different characteristics and if passenger reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction by improving provided services. In this study, we investigate customer review data for in-flight services of airline companies and draw customer models with respect to such data. In this sense, we apply two approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are grouped into categories based on features such as cabin flown types, experienced airline companies. In clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We apply multivariate regression analysis to model customer groups in both cases. During this, we try to understand how customers evaluate the given services and what dominant characteristics of in-flight services can be from the customer viewpoint.