Abstract
Understanding customers and the market in the airline industry, which has unique characteristics such as a competitive environment, diverse consumer expectations, and different service levels, is critical for marketing decision-making. Digital platforms offer valuable information sources through online reviews to companies, and it is essential to evaluate the data to evaluate the customers. The study aims to discover the topics in airline user reviews from a duality perspective by recommending reviews and not-recommending reviews. Consistent with the study aim, topic modeling methodology through BERTopic transformers-model is employed to detect the topics included in Skytrax online reviews on Airlinequality.com. 33.810 user reviews from 25 airline companies are used as the study sample. Individual topics detected by topic modeling methodology are grouped into topic groups in the study. Five main topic groups (flight experience, customer service, travel class, airline mentions, and other) for recommending status user reviews, and nine main topics groups (flight experience, service experience, customer service/operations, baggage, customer expressions, region/country-based expressions, seats, transferring process and special cases) for not-recommending status user reviews are concluded in the study.
References
- Aaker, J., & Fournier, S. (1995). A brand as a character, a partner and a person: Three perspectives on the question of brand personality. ACR North American Advances.
- Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
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- Bunchongchit, K., & Wattanacharoensil, W. (2021). Data analytics of Skytrax's airport review and ratings: Views of airport quality by passengers types. Research in Transportation Business & Management, 41, 100688. https://doi.org/10.1016/j.rtbm.2021.100688
- Canbolat, Z. N., & Pinarbasi, F. (2022). Using sentiment analysis for evaluating e-WOM: A data mining approach for marketing decision making. In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 1360-1383). IGI Global.
- Chatterjee, S., & Mandal, P. (2020). Traveler preferences from online reviews: Role of travel goals, class and culture. Tourism Management, 80, 104108. https://doi.org/10.1016/j.tourman.2020.104108
- Chen, Q., Chen, C., Hassan, S., Xing, Z., Xia, X., & Hassan, A. E. (2021). How should i improve the ui of my app? a study of user reviews of popular apps in the google play. ACM Transactions on Software Engineering and Methodology (TOSEM), 30(3), 1-38. https://doi.org/10.1145/3447808
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- Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision support systems, 54(1), 461-470. https://doi.org/10.1016/j.dss.2012.06.008
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Abstract
Rekabetçi bir ortam, farklı tüketici beklentileri ve farklı hizmet seviyeleri gibi kendine özgü özelliklere sahip havayolu endüstrisinde müşterileri ve pazarı anlamak, pazarlama karar alma süreçleri için kritik öneme sahiptir. Dijital platformlar, şirketlere çevrimiçi yorumlar aracılığıyla değerli bilgi kaynakları sunar ve müşterileri değerlendirmek için verileri değerlendirmek önem arz etmektedir. Çalışma, havayolu kullanıcı değerlendirmelerindeki konuları, öneren ve önermeyen değerlendirmeler üzerinden iki yönlü bir bakış açısından keşfetmeyi amaçlamaktadır. Çalışmanın amacına uygun olarak, Airlinequality.com'daki Skytrax çevrimiçi değerlendirmelerinde yer alan konuları tespit etmek için BERTopic dönüştürücü modeli aracılığıyla konu modelleme metodolojisi kullanılmıştır. Çalışmada örneklem olarak 25 havayolu şirketine dair 33.810 kullanıcı yorumu kullanılmıştır. Konu modelleme metodolojisi ile tespit edilen tekli konular konu gruplarında gruplanmıştır. Çalışmada öneren değerlendirmelerde beş ana konu grubuna (uçuş deneyimi, müşteri hizmetleri, seyahat sınıfı, havayolu bahsetmeleri ve diğer), önermeyen değerlendirmelerde dokuz ana konu grubuna (uçuş deneyimi, hizmet deneyimi, müşteri hizmetleri / operasyonları, bagaj, müşteri ifadeleri, bölge / ülke merkezli ifadeler, koltuklar, transfer süreci ve özel durumlar) ulaşılmıştır.
Keywords
havayolu pazarlaması kullanıcı önerisi çevrimiçi değerlendirmeler kulaktan kulağa pazarlama
References
- Aaker, J., & Fournier, S. (1995). A brand as a character, a partner and a person: Three perspectives on the question of brand personality. ACR North American Advances.
- Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
- Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of advertising research, 47(4), 398-411. https://doi.org/10.2501/S0021849907070419
- Barde, B. V., & Bainwad, A. M. (2017, June). An overview of topic modeling methods and tools. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 745-750). IEEE. https://doi.org/10.1109/ICCONS.2017.8250563
- Brochado, A., Rita, P., Oliveira, C., & Oliveira, F. (2019). Airline passengers’ perceptions of service quality: Themes in online reviews. International Journal of Contemporary Hospitality Management, 31(2), 855- 873. https://doi.org/10.1108/IJCHM-09-2017-0572
- Bunchongchit, K., & Wattanacharoensil, W. (2021). Data analytics of Skytrax's airport review and ratings: Views of airport quality by passengers types. Research in Transportation Business & Management, 41, 100688. https://doi.org/10.1016/j.rtbm.2021.100688
- Canbolat, Z. N., & Pinarbasi, F. (2022). Using sentiment analysis for evaluating e-WOM: A data mining approach for marketing decision making. In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 1360-1383). IGI Global.
- Chatterjee, S., & Mandal, P. (2020). Traveler preferences from online reviews: Role of travel goals, class and culture. Tourism Management, 80, 104108. https://doi.org/10.1016/j.tourman.2020.104108
- Chen, Q., Chen, C., Hassan, S., Xing, Z., Xia, X., & Hassan, A. E. (2021). How should i improve the ui of my app? a study of user reviews of popular apps in the google play. ACM Transactions on Software Engineering and Methodology (TOSEM), 30(3), 1-38. https://doi.org/10.1145/3447808
- Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58-70. https://doi.org/10.1016/j.ijhm.2018.04.004
- Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision support systems, 54(1), 461-470. https://doi.org/10.1016/j.dss.2012.06.008
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://arxiv.org/abs/1810.04805
- Google Colab. (2024). Welcome to Colab. Retrieved from https://colab.research.google.com/?hl=en
- Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794. https://arxiv.org/abs/2203.05794
- Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer- opinion platforms: what motivates consumers to articulate themselves on the internet?. Journal of interactive marketing, 18(1), 38-52. https://doi.org/10.1002/dir.10073
- IATA. (2024a). Industry Statistics Fact Sheet. Retrieved from https://www.iata.org/en/iata- repository/pressroom/fact-sheets/industry-statistics/
- IATA. (2024b). Global Outlook for Air Transport Deep Change. Retrieved from https://www.iata.org/en/iata- repository/publications/economic-reports/global-outlook-for-air-transport-june-2024-report/
- Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems with Applications, 116, 472-486. https://doi.org/10.1016/j.eswa.2018.09.037
- Krishnamurthy, A., & Kumar, S. R. (2018). Electronic word-of-mouth and the brand image: Exploring the moderating role of involvement through a consumer expectations lens. Journal of Retailing and Consumer Services, 43, 149-156. https://doi.org/10.1016/j.jretconser.2018.03.010
- Ladhari, R., & Michaud, M. (2015). eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions. International Journal of Hospitality Management, 46, 36-45. https://doi.org/10.1016/j.ijhm.2015.01.010
- Lee, C. C., & Hu, C. (2005). Analyzing Hotel customers' E-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing, 17(2-3), 167-181. https://doi.org/10.1300/J073v17n02_13
Details
| Primary Language | English |
|---|---|
| Subjects | Business Administration |
| Journal Section | Articles |
| Authors | |
| Publication Date | April 30, 2025 |
| Submission Date | September 26, 2024 |
| Acceptance Date | February 16, 2025 |
| Published in Issue | Year 2025 Volume: 18 Issue: 2 |
Ömer Halisdemir Universitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi (OHUIIBF) is licensed under the Creative Commons Attribution-Noncommercial-Pseudonymity License 4.0 international license.