Evaluating Airline Passengers’ Satisfaction During the COVID-19 Pandemic: A Case Study of AirAsia Services Through Sentiment Analysis and Topic Modelling

Lee Jie Yu; Nor Hasliza Md Saad; Zhu Kun; Ghada ElSayad

doi:10.21512/commit.v18i2.11364

Authors

Lee Jie Yu Universiti Sains Malaysia
Nor Hasliza Md Saad Universiti Sains Malaysia
Zhu Kun Universiti Sains Malaysia https://orcid.org/0000-0001-5546-7001
Ghada ElSayad College of Management and Technology, Arab Academy for Science, Technology and Maritime Transport (AAST) https://orcid.org/0000-0002-8502-9384

DOI:

https://doi.org/10.21512/commit.v18i2.11364

Keywords:

Airline Service, Customer Satisfaction, COVID-19 Pandemic, Sentiment Analysis, Topic Modelling

Abstract

AirAsia has emerged as a dominant force among prominent low-cost airlines in recent years. However, the COVID-19 pandemic outbreak has severely impacted airline services, including AirAsia. There is a strong need for airline services to monitor customer experience and satisfaction from online customer reviews on the website to keep pace with changing customer perceptions toward their service quality. A growing number of travelers choose to express their experiences and emotions on online customer review platforms, resulting in substantial online airline service evaluations. The research analyses 796 online customer reviews from Skytrax, a well-known online airline review website. The information hidden in customer-generated reviews is analyzed with the text mining technique, including topic modeling and sentiment analysis. The research uses the Latent Dirichlet allocation (LDA) model for topic analysis and the Valence Aware Dictionary for Sentiment Reasoning (VADER) model for sentiment analysis. The sentiment ratio for AirAsia’s online reviews is approximately 59% positive and 41% negative. Only four reviews are neutral. The findings indicate that the online review of AirAsia has a greater proportion of positive sentiments than negative sentiments. In addition, the topic modeling shows hidden topics with the top high-probability keywords concerned with interior and seat, baggage, online service, staff service, flight schedule, and refund. The research demonstrates using sentiment analysis and topic modeling on customer review data as a more thorough alternative to survey-based models for researching airline service. The research contributes to the methodological advancements in text mining analysis and expands the current knowledge of customer review data.

Dimensions

Plum Analytics

Author Biographies

Lee Jie Yu, Universiti Sains Malaysia

Business Analytics, School of Management

Nor Hasliza Md Saad, Universiti Sains Malaysia

Operations Management & Business Analytics Sections, School of Management

Zhu Kun, Universiti Sains Malaysia

Knowledge Engineering, School of Computer Sciences

Ghada ElSayad, College of Management and Technology, Arab Academy for Science, Technology and Maritime Transport (AAST)

Business Information Systems Department

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