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Forecasting airline passengers’ satisfaction based on sentiments and ratings: An application of VADER and machine learning techniques

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  • Murugesan, R.
  • A P, Rekha
  • N, Nitish
  • Balanathan, Raghavan

Abstract

To the best of the authors' knowledge, research predicting airline passengers' satisfaction based on their sentiments and ratings is seldom sighted. Additionally, the literature reveals that most studies have primarily concentrated on specific airlines or routes, neglecting to conduct a comparative analysis of satisfaction levels across numerous airlines and routes. Hence, this research aims to predict passengers' satisfaction by combining the sentiment of their reviews and ratings on various parameters like food, entertainment, seat comfort, ground service, and value for money. Using the "Skytrax Airline Reviews" dataset, which contains data about 81 airlines and 64440 reviews, our research analyzes and predicts airline passengers' satisfaction based on sentiments and ratings using nine popular machine learning techniques. The study found that the LightGBM obtains an accuracy of 97 percent in predicting customer satisfaction. The results further reveal that 'value for money' and 'ground service' are crucial factors in determining the passengers' satisfaction, whereas 'entertainment' had no significant impact. Our study thus provides a valuable tool for predicting airline industry customer satisfaction and gives insight into the factors contributing to passenger satisfaction. These findings can further help airlines better understand their passengers' needs and improve their services accordingly.

Suggested Citation

  • Murugesan, R. & A P, Rekha & N, Nitish & Balanathan, Raghavan, 2024. "Forecasting airline passengers’ satisfaction based on sentiments and ratings: An application of VADER and machine learning techniques," Journal of Air Transport Management, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jaitra:v:120:y:2024:i:c:s0969699724001339
    DOI: 10.1016/j.jairtraman.2024.102668
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    References listed on IDEAS

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    1. Jou, Rong-Chang & Kuo, Chung-Wei & Chiu, Yi-Chun, 2022. "Bidding behaviors for international airline seats in short/long distance flights," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 55-79.
    2. Shiwakoti, Nirajan & Jiang, Hongwei & Nguyen, An Duong, 2022. "Passengers’ perception of safety and its relationship with demographics, service quality, satisfaction and loyalty in airlines sector - A case study of Vietnam to Australia route," Transport Policy, Elsevier, vol. 124(C), pages 194-202.
    3. Li, Lingyao & Mao, Yujie & Wang, Yu & Ma, Zihui, 2022. "How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis," Journal of Air Transport Management, Elsevier, vol. 105(C).
    4. Singh, Balgopal, 2021. "Predicting airline passengers’ loyalty using artificial neural network theory," Journal of Air Transport Management, Elsevier, vol. 94(C).
    5. Jose Ribamar Siqueira & Michael Bendixen & Felipe Reinoso-Carvalho & Raffaele Campo, 2023. "Key drivers of brand trust in a Latin American airline: the impact of Colombia’s Avianca customer experience," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 186-201, June.
    6. 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.
    7. Lucini, Filipe R. & Tonetto, Leandro M. & Fogliatto, Flavio S. & Anzanello, Michel J., 2020. "Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews," Journal of Air Transport Management, Elsevier, vol. 83(C).
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