Brand position in the eyes of customers: assessment of selected airlines by the passengers' online reviews
DOI:
https://doi.org/10.24136/cxy.2022.001Keywords:
data mining, text mining, branch, brand, opinion, R, client, airline, sentiment analysisAbstract
Motivation: The motivation to write an article on airlines was the desire to rank them based on customer reviews and see how these reviews reflect the actual brand image. The opinions that companies collect about themselves have a very strong power when it comes to building its reputation.
Aim: The aim of the study was to use digital transformation and transform raw data into specific information that expressed customer emotions to create a profile of selected airlines. A secondary goal of the article was also to check how the analyzed airlines perform in similar areas.
Materials and methods: The data used for the analysis was collected from the eSky.com website and covers the 2019-2020 period. The airlines concerned by the customer reviews were LOT, Ryanair, Wizzair, Czarter, EasyJet, Lufthansa and Laudamotion. Their selection was dictated by the number of opinions necessary to conduct the analysis. The research based on the use of data mining techniques, but it should be noted that most of it uses text mining tools. Topic modelling was used to prepare the data properly and assign each word to groups with similar themes. In order to obtain information whether a given opinion has a positive, negative or neutral tenor, sentiment analysis was used. The final part of the analysis was based on the net sentiment score indicator. The entire analysis was carried out in the R-Studio.
Results: The most common subjects of opinions written by customers were "delay", "service", "boarding" and "airline". It was confirmed that the opinions of each airline concern different topics, although some common topics were noticeable. Two topics were repeated among the 7 analyzed airlines: "service" and "delay". Based on the sentiment analysis, for the Ryanair airline the percentage of negative opinions was highest and equal to 35%, almost 40%, of neutral opinions fell on the WizzAir airline and the largest percentage of positive feedback, as much as 46%, was attributed to EasyJet. EasyJet line looks the best in the eyes of customers. The line that evoked uniformly positive, negative and neutral emotions in the opinions was Ryanair.
Downloads
References
Allen, J., Bellizzi, M.G., Eboli, L., Forciniti, C., & Mazzulla, G.. (2020). Air transport service quality factors: a systematic literature review. Transportation Research Procedia, 45, 218?225. https://doi.org/10.1016/j.trpro.2020.03.010.
Baj-Rogowska, A. (2017). Sentiment analysis of Facebook posts: the Uber case. Eighth International Conference on Intelligent Computing and Information Systems (pp. 391?395), https://doi.org/10.1109/INTELCIS.2017.8260068.
Baj-Rogowska, A. (2020). Evaluation of a company?s image on social media using the Net Sentiment Rate. In E. Lechman, & M. Popowska (Eds.), Society and technology: opportunities and challenges (pp. 202?218). https://doi.org/10.4324/9780429278945.
Chou, C.-C., Liu, L.-J., Huang, S.-F., Yih, J.-M., & Han T.-C. (2011). An evaluation of airline service quality using the fuzzy weighted SERVQUAL method. Applied Soft Computing, 11(2), 2117?2128. https://doi.org/10.1016/j.asoc.2010.07.010.
D?Andrea, A., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3), 2015, 26?33. https://doi.org/10.5120/ijca2015905866.
Eachempati, P., Srivastava, P.R., Kumar, A., de Prat, J.M, & Delen, D. (2022). Can customer sentiment impact firm value: an integrated text mining approach. Technological Forecasting and Social Change, 174, 121265. https://doi.org/10.1016/j.techfore.2021.121265.
eSky.com. (2022). Retrieved 01.03.2022 from https://www.esky.com.
Farzadnia, S., Raessi Vanani, I. (2022). Identification of opinion trends using sentiment analysis of airlines passengers? reviews. Journal of Air Transport Management, 103, 102232. https://doi.org/10.1016/j.jairtraman.2022.102232.
Jiang, H., & Zhang, Y. (2016). An investigation of service quality, customer satisfaction and loyalty in China?s airline market. Journal of Air Transport Management, 57, 80?88. https://doi.org/10.1016/j.jairtraman.2016.07.008.
Leon, S., & Martín, J.C. (2020). A fuzzy segmentation analysis of airline passengers in the U.S. based on service satisfaction. Research in Transportation Business and Management, 37, 100550. https://doi.org/10.1016/j.rtbm.2020.100550.
Liu, B. (2011). Opinion mining and sentiment analysis. In: B. Liu, Web data mining. Data-centric systems and applications (pp. 459?526). Springer. https://doi.org/10.1007/978-3-642-19460-3_11.
Noviantoro, T., & Huang, J.-P. (2021). Investigating airline passenger satisfaction: data mining method. Research in Transportation Business and Management, 43, 100726. https://doi.org/10.1016/j.rtbm.2021.100726.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Catallaxy
This work is licensed under a Creative Commons Attribution 4.0 International License.