Airline Service Quality Evaluation Based on Customer Review Using Machine Learning Approach and Sentiment Analysis

Woojin Jeon, Yebin Lee, Youngjung Geum


The airline industry faces with significant competition due to the rise of technology innovation and diversified customer needs. Therefore, continuous quality management is essential to gain competitive advantages. For this reason, there have been various studies to measure and manage service quality using customer reviews. However, previous studies have focused on measuring customer satisfaction only, neglecting systematic management between customer expectations and perception based on customer reviews. In response, this study suggests a framework to identify relevant criteria for service quality management, measure the importance, and assess the customer perception based on customer reviews. Machine learning techniques, topic models, and sentiment analysis are used for this study.
This study can be used as an important strategic tool for evaluating service quality by identifying important factors for airline customer satisfaction while presenting a framework for identifying each airline’s current service level.

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