Marka Sıralamasının Ötesinde: Havayolu Sektöründe Müşteri Deneyimini Ölçmek İçin Duygu Analizi ve Konu Modelleme Yaklaşımı

Öz

Bu çalışma, havayolu yolcu yorumlarını analiz ederek marka performansının müşteri algılarına nasıl yansıdığını değerlendirmeyi amaçlamaktadır. Müşteri memnuniyetini ve hizmet kalitesini ölçmek için duygu analizi ve konu modelleme teknikleri kullanılmıştır. Beş büyük havayolu şirketinden alınan çevrimiçi yorumların kapsamlı bir analizi, en sık şikâyet edilen ve övülen hizmet yönlerine odaklanarak duygu dağılımlarını belirlemek için yapılmıştır. Bulgular, özellikle biletleme, rezervasyon ve uçuş deneyimleri konusunda American Airlines ve United Airlines'ın önemli ölçüde olumsuz geri bildirim aldığını göstermektedir. Buna karşılık, Lufthansa ve Emirates, özellikle koltuk ve kabin konforu açısından daha dengeli bir duygu dağılımı sergilemiştir. Delta Airlines'ın uçuş deneyimi ve hizmet kalitesiyle ilgili olarak nispeten daha yüksek oranda olumlu geri bildirim aldığı da dikkat çekicidir. Bu çalışma, müşteri geri bildirimlerini analiz ederek hizmet kalitesinin iyileştirilmesine katkıda bulunmakta ve böylece havayolu hizmetlerinin yolcu beklentileriyle uyumlu olmasını sağlayarak stratejik karar alma süreçlerini desteklemektedir. Ayrıca, doğal dil işleme ve makine öğrenimi tabanlı analizlerin pazarlama ve marka yönetimi süreçlerine entegre edilmesinin önemini vurgulamaktadır.

Anahtar Kelimeler

• Duygu analizi
• Konu modelleme
• Müşteri geri bildirimi
• Havayolu sektörü
• Hizmet kalitesi

Beyond Brand Ranking: A Sentiment Analysis and Topic Modeling Approach to Measuring Customer Experience in the Airline Industry

Öz

The present study aims to evaluate how brand performance is reflected in customer perceptions by analyzing airline passenger reviews. In order to measure customer satisfaction and service quality, sentiment analysis and topic modelling techniques were employed. A comprehensive analysis of online reviews from five major airline companies was conducted to identify sentiment distributions, with a focus on the most frequently complained about and praised service aspects. The findings indicate that American Airlines and United Airlines received substantial negative feedback, particularly regarding ticketing, reservations and flight experiences. In contrast, Lufthansa and Emirates demonstrated a more balanced sentiment distribution, especially in terms of seat and cabin comfort. It is worthy of note that Delta Airlines received a comparatively higher proportion of positive feedback with regard to flight experience and service quality. The present study makes a contribution to the improvement of service quality by means of analyzing customer feedback and thereby supports strategic decision-making by ensuring that airline services are aligned with passenger expectations. Moreover, it emphasizes the significance of incorporating natural language processing and machine learning-based analyses into marketing and brand management processes.

Anahtar Kelimeler

• Sentiment analysis
• Topic modeling
• Customer feedback
• Airline industry
• Service quality

Kaynakça

1. Abdelrazek, A., Eid, Y., Gawish, E., Medhat, W., and Hassan, A. (2023). Topic modeling algorithms and applications: A survey. Information Systems, 112, 102131.
2. Adeborna, E., and Siau, K. (2014). An approach to sentiment analysis-the case of airline quality rating. PACIS 2014 Proceedings (pp. 363). Association for Information Systems.
3. Aljedaani, W., Rustam, F., Mkaouer, M. W., Ghallab, A., Rupapara, V., Washington, P. B., and Ashraf, I. (2022). Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowledge-Based Systems, 255, 109780.
4. Anitsal, M. M., Anitsal, I., and Anitsal, S. (2019). Is your business sustainable? A sentiment analysis of air passengers of top 10 US-based airlines. Journal of Global Scholars of Marketing Science, 29(1), 25-41.
5. Ansari, G., Ahmad, T., and Doja, M. N. (2019). Hybrid filter-wrapper feature selection method for sentiment classification. Arabian Journal for Science and Engineering, 44, 9191-9208.
6. Badanik, B., Remenysegova, R., and Kazda, A. (2023). Sentimental approach to airline service quality evaluation. Aerospace, 10(10), 883.
7. Bae, W., and Chi, J. (2022). Content analysis of passengers’ perceptions of airport service quality: The case of Honolulu International Airport. Journal of Risk and Financial Management, 15(1), 5.
8. Bergamaschi, S., and Po, L. (2015). Comparing LDA and LSA topic models for content-based movie recommendation systems. In: Monfort, V., and Krempels, K. H. (eds) Web Information Systems and Technologies. WEBIST 2014. Lecture Notes in Business Information Processing, vol 226. Springer.

9. Bernabé-Moreno, J., Tejeda-Lorente, A., Herce-Zelaya, J., Porcel, C., and Herrera-Viedma, E. (2020). A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary. Knowledge-Based Systems, 190, 105236.
10. Bhatia, P., Ji, Y., and Eisenstein, J. (2015). Better document-level sentiment analysis from rst discourse parsing. arXiv preprint arXiv:1509.01599,2015. https://arxiv.org/pdf/1509.01599
11. Birjali, M., Kasri, M., and Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
12. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022.
13. Chaturvedi, I., Cambria, E., Welsch, R. E., and Herrera, F. (2018). Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Information Fusion, 44, 65-77.
14. Crain, S. P., Zhou, K., Yang, S. H., and Zha, H. (2012). Dimensionality reduction and topic modeling. In: C. C. Aggarwal, and C. Zhai, Mining text data (pp. 129-155). Springer.
15. Farzadnia, S., and Vanani, I. R. (2022). Identification of opinion trends using sentiment analysis of airlines passengers' reviews. Journal of Air Transport Management, 103, 102232.
16. Fortune. (2024). Fortune Global 500: Airlines. https://fortune.com/ranking/global500/?sector=Transportation&fg500_industry=Airlines
17. Gandomi, A., and Haider, M. (2015). Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management, 35(2), 137-144.
18. Gouthami, S., and Hegde, N. P. (2023). Feature selection based sentiment analysis on US Airline Twitter data. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 1735-1746. Guo, Y., Barnes, S. J., and Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
19. Hasib, K. M., Habib, M. A., Towhid, N. A., and Showrov, M. I. (2021). A novel deep learning-based sentiment analysis of twitter data for us airline service. International Conference on Information and Communication Technology for Sustainable Development (pp. 450-455). IEEE. https://doi.org/10.1109/ICICT4SD50815.2021.9396879
20. Hutto, C. J., and Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the Eighth International Conference on Weblogs and Social Media, 8(1), 216-225.
21. Iddrisu, A. M., Mensah, S., Boafo, F., Yeluripati, G. R., and Kudjo, P. (2023). A sentiment analysis framework to classify instances of sarcastic sentiments within the aviation sector. International Journal of Information Management Data Insights, 3(2), 100180.
22. Idris, S. L., and Mohamad, M. (2023). A study on sentiment analysis on airline quality services: A conceptual paper. Information Management and Business Review, 15(4), 564-576.
23. Iqbal, Z., Yadav, M., and Masood, I. S. (2020). Implementation of supervised learning techniques for sentiment analysis of customer Tweets on airline services. International Journal of Engineering Applied Sciences and Technology, 5(3), 351-357.
24. Kim, J., Lee, H., and Lee, H. (2023). Mining the determinants of review helpfulness: A novel approach using intelligent feature engineering and explainable AI. Data Technologies and Applications, 57(1), 108-130.
25. Koçak, B. B. (2018). Türk havayolu işletmelerinin yolcu taşıma hizmetlerine yönelik algı haritalarının konumlandırma konsepti ile ilişkisinin Twitter üzerinden analizi. Doctoral dissertation, Anadolu University.
26. Kumar, S., and Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6, 62.
27. Kwon, H. J., Ban, H. J., Jun, J. K., and Kim, H. S. (2021). Topic modeling and sentiment analysis of online review for airlines. Information, 12(2), 78.
28. Lemon, K. N., and Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96.
29. Liu, B., and Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In: C. C. Aggarwal, and C. Zhai, Mining text data (pp. 415-453). Springer. https://doi.org/10.1007/978-1-4614-3223-4_13
30. Loria, S. (2025). Textblob documentation. Release 0.19.0.
31. Nam, S., and Lee, H. C. (2019). A text analytics-based importance performance analysis and its application to airline service. Sustainability, 11(21), 6153.
32. Nguyen, V. H., and Ho, T. (2021). Analyzing customer experience in hotel services using topic modeling. Journal of Information Processing Systems, 17(3), 586-598.
33. Nielsen, F. A. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903. https://doi.org/10.48550/arXiv.1103.2903
34. Pang, B., and Lee, L. (2009). Opinion mining and sentiment analysis. Computational Linguistics, 35(2), 311-312.
35. Piryani, R., Madhavi, D., and Singh, V. K. (2017). Analytical mapping of opinion mining and sentiment analysis research during 2000-2015. Information Processing & Management, 53(1), 122-150.
36. Raihen, M. N., and Akter, S. (2024). Sentiment analysis of passenger feedback on US airlines using machine learning classification methods. World Journal of Advanced Research and Reviews, 23(1), 2260-2273.
37. Riaz, S., Fatima, M., Kamran, M., and Nisar, M. W. (2019). Opinion mining on large scale data using sentiment analysis and k-means clustering. Cluster Computing, 22(Suppl 3), 7149-7164. https://doi.org/10.1007/s10586-017-1077-z
38. Saad, A. I. (2020). Opinion mining on US Airline Twitter data using machine learning techniques. In 2020 16th International Computer Engineering Conference (ICENCO) (pp. 59-63). IEEE. https://doi.org/10.1109/ICENCO49778.2020.9357390
39. Samir, H. A., Abd-Elmegid, L., and Marie, M. (2023). Sentiment analysis model for airline customers’ feedback using deep learning techniques. International Journal of Engineering Business Management, 15, 1-23.
40. Sánchez-Rada, J. F., and Iglesias, C. A. (2019). Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion, 52, 344-356.
41. Shahid, R., Mozumder, M. A., Sweet, M. M., Hasan, M., Alam, M., Rahman, M. A., and Islam, M. R. (2024). Predicting customer loyalty in the airline industry: A machine learning approach integrating sentiment analysis and user experience. International Journal on Computational Engineering, 1(2), 50-54.
42. Sim, H. J. (2023). Okanagan sentiment analysis of airlines reviews. Doctoral dissertation, University of British Columbia.
43. Song, R., Shi, W., Qin, W., Xue, X., and Jin, H. (2024). Exploring passengers’ emotions and satisfaction: A comparative analysis of airport and railway station through online reviews. Sustainability, 16(5), 2108.
44. Srinivas, S., and Ramachandiran, S. (2020). Discovering airline-specific business intelligence from online passenger reviews: An unsupervised text analytics approach. https://doi.org/10.48550/arXiv.2012.08000
45. Srinivas, S., and Ramachandiran, S. (2024). Passenger intelligence as a competitive opportunity: Unsupervised text analytics for discovering airline-specific insights from online reviews. Annals of Operations Research, 333, 1045-1075.
46. Sutherland, I., Sim, Y., Lee, S. K., Byun, J., and Kiatkawsin, K. (2020). Topic modeling of online accommodation reviews via latent dirichlet allocation. Sustainability, 12(5), 1821.
47. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., and Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307.
48. Wankhade, M., Rao, A. C., and Kulkarni, C. (2022). A survey on sentiment analysis methods, applications and challenges. Artificial Intelligence Review, 55, 5731-5780.
49. Xin, Z. (2024). A natural language processing and deep learning approach to topic modeling and sentiment prediction of Singapore Airlines reviews. Doctoral dissertation, UCLA.
50. Yang, S., and Zhang, H. (2018). Text mining of Twitter data using a latent dirichlet allocation topic model and sentiment analysis. International Journal of Computer and Information Engineering, 12(7), 525-529.
51. Yue, L., Chen, W., Li, X., Zuo, W., and Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663.