A Data-Driven Model for Ranking Airlines Using Multicriteria Decision-making and Sentiment Analysis

Document Type : Research Paper

Authors

1 MSc. Student, Department of Industrial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

2 Associate Prof., Department of Socio-economic Systems, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

3 Associate Prof., Department of Industrial Management, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran.

10.22059/imj.2024.373514.1008133

Abstract

Objective
Analyzing customer feedback from airline websites is more effective than traditional questionnaire-based methods, as these websites provide highly accurate and comprehensive information. Using big data technology, these websites collect and analyze millions of passenger reviews, offering more accurate information about customer experiences. Online reviews, as an open platform, provide the opportunity for employers to receive criticisms and suggestions, and due to their high volume and widespread dissemination, they can serve as a valuable source for analyzing customer sentiments and needs. Therefore, this study aims to propose a data-driven framework for ranking airlines, combining Multi-Criteria Decision Making (MCDM) methods with Sentiment Analysis (SA) at the aspect level. The main objective of this research is to evaluate the quality of airline services and rank them based on the reviews recorded on the SKYTRAX website from the users' sentiments hidden in their reviews.
 
Methods
The proposed framework consists of three stages: Stage (1): After collecting the data and preprocessing the text, airline features were extracted using the High Attribute Clustering (HAC) algorithm. Stage (2): Sentiment orientation in each airline was identified to calculate the performance scores for each airline. Stage (3): Airlines were ranked using the TOPSIS method based on intuitive fuzzy numbers, considering the scores obtained in the second stage. Intuitive Fuzzy Sets (IFS) were used to represent effective customer opinions, including hesitant phrases in the decision matrix. Also, the criteria weights were determined through the entropy method.
 
Results
The performance of 10 airlines was analyzed, and ranked accordingly. The results show that for economy class airlines, with a weight of 0.17, features such as customer service, legroom, flight delays, and security inspection, each with a weight of 0.11, are equally important to passengers as other features. According to the results, Middle East Airlines demonstrates the highest performance among the ten airlines (Saudi Arabian Airlines, Kuwait Airways, Oman Air, Iran Air, Egyptair, Royal Jordanian Airlines, Middle East Airlines, Pegasus Airlines, flydubai, and Air Arabia) i.e. it has the closest distance to the positive ideal solution of the fuzzy intuitive set and the farthest distance from the negative ideal solution of the fuzzy intuitive set. While Pegasus Airlines has the closest distance to the negative ideal solution and the farthest distance from the positive ideal solution, its performance is the lowest among the four airlines.
 
Conclusion
This research greatly assists Middle Eastern airlines in seeking areas for improvement and in comparing their performance with their competitors to achieve a better competitive advantage in the market. The data from this research can be used to create a recommendation system for travelers helping them choose airlines that best align with their expectations, preferences, and travel goals. This can take into account factors such as budget, destination, and flight class, which can help airline managers better understand and meet customer needs.

Keywords

Main Subjects

References

 
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Industrial Management Journal
Volume 16, Issue 4
2024
Pages 560-596

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