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 analyzes 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

References

IATA, “The importance of air transport to Malaysia,” 2018. [Online]. Available: https: //www.iata.org/en/iata-repository/publications/economic-reports/malaysia--value-of-aviation/

CAPA, “Malaysia aviation: Growth slows, profits under pressure,” 2019. [Online]. Available: https: //shorturl.at/tfRvK

S. F. S. Yahya, “The impact of COVID-19 on Malaysia’s airline industry,” 2020. [Online]. Available: https://shorturl.at/QnT4t

B. Vinod, “The COVID-19 pandemic and airline cash flow,” Journal of Revenue and Pricing Management, vol. 19, no. 4, pp. 228–229, 2020.

T. H. Hassan and A. E. Salem, “Impact of service quality of low-cost carriers on airline image and consumers’ satisfaction and loyalty during the COVID-19 outbreak,” International Journal of Environmental Research and Public Health, vol. 19, no. 1, pp. 1–16, 2022.

K. Tegar, A. Lestari, and S. W. Pratiwi, “An analysis of airlines customer satisfaction by improving customer service performance,” in Proceedings of the Conference on Global Research on Sustainable Transport (GROST 2017). Atlantis Press, 2017, pp. 619–628.

M. Giatsoglou, M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. Chatzisavvas, “Sentiment analysis leveraging emotions and word embeddings,” Expert Systems with Applications, vol. 69, pp. 214–224, 2017.

A. K. Kar, “What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model”,” Information Systems Frontiers, vol. 23, no. 5, pp. 1341–1361, 2021.

M. Siering, A. V. Deokar, and C. Janze, “Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews,” Decision Support Systems, vol. 107, pp. 52–63.

B. A. Sparks, H. E. Perkins, and R. Buckley, “Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior,” Tourism Management, vol. 39, pp. 1–9.

Q. Ye, R. Law, B. Gu, and W. Chen, “The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings,” Computers in Human Behavior, vol. 27, no. 2, pp. 634–639, 2011.

K. Zhu and N. H. Samsudin, “Attention-based spatialized word embedding Bi-LSTM model for sentiment analysis,” Pertanika Journal of Science & Technology, vol. 32, no. 1, pp. 79–98, 2024.

N. Korfiatis, P. Stamolampros, P. Kourouthanassis, and V. Sagiadinos, “Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews,” Expert Systems with Applications, vol. 116, pp. 472–486, 2019.

M. Zhang, B. Fan, N. Zhang, W. Wang, and W. Fan, “Mining product innovation ideas from online reviews,” Information Processing & Management, vol. 58, no. 1, 2021.

I. Bueno, R. A. Carrasco, R. Ure˜na, and E. Herrera-Viedma, “A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in emarketing situations,” Information Sciences, vol. 589, pp. 300–320, 2022.

M. Ferrer-Est´evez and R. Chalmeta, “Sustainable customer relationship management,” Marketing Intelligence & Planning, vol. 41, no. 2, pp. 244–262, 2023.

M. J. Noh and K. T. Lee, “An analysis of the relationship between quality and user acceptance in smartphone apps,” Information Systems and E-Business Management, vol. 14, pp. 273–291, 2016.

X. Fang and J. Zhan, “Sentiment analysis using product review data,” Journal of Big Data, vol. 2, pp. 1–14, 2015.

V. Chang, L. Liu, Q. Xu, T. Li, and C. H. Hsu, “An improved model for sentiment analysis on luxury hotel review,” Expert Systems, vol. 40, no. 2, 2023.

L. A. D. A. Lari, S. Iyanna, and F. Jabeen, “Islamic and Muslim tourism: Service quality and theme parks in the UAE,” Tourism Review, vol. 75, no. 2, pp. 402–413, 2020.

J. M. Luo, H. Q. Vu, G. Li, and R. Law, “Topic modelling for theme park online reviews: Analysis of Disneyland,” Journal of Travel & Tourism Marketing, vol. 37, no. 2, pp. 272–285, 2020.

A. Brasoveanu, M. Moodie, and R. Agrawal, “Textual evidence for the perfunctoriness of independent medical reviews,” in KiML, 2020, pp. 1–9.

Y. Cheng, L. Yao, G. Xiang, G. Zhang, T. Tang, and L. Zhong, “Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism,” IEEE Access, vol. 8, pp. 134 964–134 975, 2020.

F. M. Khan, S. A. Khan, K. Shamim, Y. Gupta, and S. I. Sherwani, “Analysing customers’ reviews and ratings for online food deliveries: A text mining approach,” International Journal of Consumer Studies, vol. 47, no. 3, pp. 953–976, 2023.

A. Bilal, H. T. Mirza, and I. Hussain, “Identifying significant textual features in titles of Google play store applications and their influence on user review rating,” Knowledge and Information Systems, vol. 65, no. 3, pp. 1159–1178, 2023.

A. Onan, “Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach,” Computer Applications in Engineering Education, vol. 29, no. 3, pp. 572–589, 2021.

M. A. Ibrahim, S. Arifin, I. G. A. A. Yudistira, R. Nariswari, A. A. Abdillah, N. P. Murnaka, and P. W. Prasetyo, “An explainable AI model for hate speech detection on Indonesian Twitter,” CommIT (Communication and Information Technology) Journal, vol. 16, no. 2, pp. 175–182, 2022.

S. Lee, S. Lee, and H. Baek, “Does the dispersion of online review ratings affect review helpfulness?” Computers in Human Behavior, vol. 117, 2020.

B. Dahal, S. A. P. Kumar, and Z. Li, “Topic modeling and sentiment analysis of global climate change tweets,” Social Network Analysis and Mining, vol. 9, pp. 1–20, 2019.

I. Jun, Z. Feng, R. Avanasi, R. A. Brain, M. Prosperi, and J. Bian, “Evaluating the perceptions of pesticide use, safety, and regulation and identifying common pesticide-related topics on Twitter,” Integrated Environmental Assessment and Management, vol. 19, no. 6, pp. 1581–1599, 2023.

A. K. Juswil, S. Nofrima, and H. A. Saputra, “Political communication patterns through social media: A case of an Indonesian presidential staff Twitter account,” CommIT (Communication and Information Technology) Journal, vol. 16, no. 2, pp. 203–212, 2022.

N. H. M. Saad, A. S. Z. Abidi, Z. Yaacob, M. M. M. Ali, and Z. Kun, “Twitter sentiment analysis on Meta: A lexicon-based analysis using Rapidminer ,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 6, pp. 259–270, 2023.

A. N. Sutranggono and E. M. Imah, “Tweets emotions analysis of community activities restriction as COVID-19 policy in Indonesia using support vector machine,” CommIT (Communication and Information Technology) Journal, vol. 17, no. 1, pp. 13–25, 2023.

S. Zheng, J. Wang, C. Sun, X. Zhang, and M. E. Kahn, “Air pollution lowers Chinese urbanites’ expressed happiness on social media,” Nature Human Behaviour, vol. 3, no. 3, pp. 237–243, 2019.

X. Xu, B. Li, Y. Shen, B. Luo, C. Zhang, and F. Hao, “Short text classification based on hierarchical heterogeneous graph and LDA fusion,” Electronics, vol. 12, no. 12, pp. 1–14, 2023.

Y. Wen, Y. Liang, and X. Zhu, “Sentiment analysis of hotel online reviews using the BERT model and ERNIE model–Data from China,” PLoS ONE, vol. 18, no. 3, pp. 1–14, 2023.

D. T. Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Binary klasifikasi dengan Word2vec pada media online,” Creative Information Technology Journal, vol. 8, no. 1, pp. 64–77, 2021.

A. Knorr, “Big data, customer relationship and revenue management in the airline industry: What future role for frequent flyer programs?” Review of Integrative Business and Economics Research, vol. 8, no. 2, pp. 38–51, 2019.

M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “LSTM, VADER and TF-IDF based hybrid sentiment analysis model,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, pp. 265–275, 2021.

H. J. Kwon, H. J. Ban, J. K. Jun, and H. S. Kim, “Topic modeling and sentiment analysis of online review for airlines,” Information, vol. 12, no. 2, pp. 1–14, 2021.

F. R. Lucini, L. M. Tonetto, F. S. Fogliatto, and M. J. Anzanello, “Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews,” vol. 83, 2019.

I. A. Aditya, F. N. Haryadi, I. Haryani, I. Rachmawati, D. P. Ramadhani, T. Tantra, and A. Alamsyah, “Understanding service quality concerns from public discourse in Indonesia state electric company,” Heliyon, vol. 9, no. 8, pp. 1–14, 2023.

S. Q. Ong, M. B. M. Pauzi, and K. H. Gan, “Text mining and determinants of sentiments towards the COVID-19 vaccine booster of Twitter users in Malaysia,” Healthcare, vol. 10, no. 6, pp. 1–12, 2022.

D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.

B. Sun, H. Mao, and C. Yin, “How to identify product defects and segment consumer groups on an online auto forum,” International Journal ofConsumer Studies, vol. 46, no. 6, pp. 2270–2287, 2022.

R. Hubbard, “P-values,” in International encyclopedia of statistical science. Springer, 2011, pp. 1144–1145.

A. I. Kadhim, “An evaluation of preprocessing techniques for text classification,” International Journal of Computer Science and Information Security (IJCSIS), vol. 16, no. 6, pp. 22–32, 2018.

A. Ghermandi and M. Sinclair, “Passive crowdsourcing of social media in environmental research: A systematic map,” Global Environmental Change, vol. 55, pp. 36–47, 2019.

S. Mishra and D. Collier, “A framework for generating annotated social media corpora with demographics, stance, civility, and topicality,” 2020. [Online]. Available: http://arxiv.org/abs/2012.05444

C. Singla, F. N. Al-Wesabi, Y. S. Pathania, B. S. Alfurhood, A. M. Hilal, M. Rizwanullah, M. A. Hamza, and M. Mahzari, “An optimized deep learning model for emotion classification in Tweets,” Computers, Materials & Continua, vol. 70, no. 3, pp. 6365–6380, 2022.

E. Haddi, X. Liu, and Y. Shi, “The role of text pre-processing in sentiment analysis,” Procedia Computer Science, vol. 17, pp. 26–32, 2013.

J. Dehler-Holland, M. Okoh, and D. Keles, “Assessing technology legitimacy with topic models and sentiment analysis–The case of wind power in Germany,” Technological Forecasting and Social Change, vol. 175, pp. 1–21, 2022.

J. Hartmann, M. Heitmann, C. Siebert, and C. Schamp, “More than a feeling: Accuracy and application of sentiment analysis,” International Journal of Research in Marketing, vol. 40, no. 1, pp. 75–87, 2023.

C. Hutto and E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the International AAAI Conference on Web and Social Media, 2014, pp. 216–225.

A. I. Almulhim, “Understanding public awareness and attitudes toward renewable energy resources in Saudi Arabia,” Renewable Energy, vol. 192, pp. 572–582, 2022.

S. K. Trivedi, S. Vishnu, A. Singh, and M. Yadav, “Research trends in sustainable e-payment systems: A study using topic modeling approach,” IEEE Transactions on Engineering Management, vol. 71, pp. 7511–7525, 2023.

X. Wei and V. Taecharungroj, “How to improve learning experience in MOOCs an analysis of online reviews of business courses on Coursera,” The International Journal of Management Education, vol. 20, no. 3, 2022.