DOI QR코드

DOI QR Code

머신러닝과 감성분석을 활용한 고객 리뷰 기반 항공 서비스 품질 평가

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

  • Jeon, Woojin (Seoul National University of Science and Technology) ;
  • Lee, Yebin (Seoul National University of Science and Technology) ;
  • Geum, Youngjung (Department of Industrial & Systems Engineering, Seoul National University of Science and Technology)
  • 투고 : 2021.07.06
  • 심사 : 2021.10.05
  • 발행 : 2021.11.30

초록

국제 항공 시장이 꾸준히 성장함에 따라 항공업계의 경쟁이 더욱 심화되고 있다. 경쟁 우위의 원천을 얻기 위해 서비스의 품질 평가는 필수적이며, 이에 다양한 연구에서 고객 리뷰를 바탕으로 서비스 품질을 측정하는 시도를 지속해 왔다. 그러나 고객 리뷰 데이터를 기반으로 기대와 지각 수준의 차이를 파악하고 전략적 방향을 제시하는 연구는 미흡한 실정이다. 본 연구에서는 항공사 서비스를 대상으로 차원별 중요도를 머신러닝을 통해 측정하고, 차원별 지각 수준을 감성분석을 통해 분석한다. 차원별 중요도와 지각 수준의 결과를 활용하여 항공사별 서비스의 성과를 측정하기 위한 전략 매트릭스를 제시하고, 이를 통해 각 항공사의 품질 분석을 수행한다. 본 연구는 항공사의 고객만족을 결정하는 중요한 요인을 파악하는 동시에, 각 항공사의 현재 서비스 수준을 파악하는 틀을 제시함으로써 서비스 품질 평가의 중요한 도구로 활용될 수 있다.

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.

키워드

과제정보

본 연구는 교육부 및 한국연구재단의 지원을 받아 수행된 연구임(2020R1I1A2070429).

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