1. Adam, I. (2021). Negative tourist-to-tourist interactions, value destruction, satisfaction, and post consumption behavioral intention. Journal of Destination Marketing & Management, 20, Article 100557. https://doi.org/10.1016/j.jdmm.2021.100557
2. Alsayat, A. (2023). Customer decision-making analysis based on big social data using machine learning: A case study of hotels in Mecca. Neural Computing and Applications, 35(6), 4701–4722. https://doi.org/10.1007/s00521-022-07992-x
3. Amat-Lefort, N., Barravecchia, F., & Mastrogiacomo, L. (2023). Quality 4.0: Big data analytics to explore service quality attributes and their relation to user sentiment in Airbnb reviews. International Journal of Quality & Reliability Management, 40(4), 990–1008. https://doi.org/10.1108/IJQRM-01-2022-0024
4. Ansari, A., & Asadi, A. (2016). Assessing tourist loyalty to destinations using a data mining approach: Domestic tourists in Isfahan. Tourism Management Studies Quarterly, 11(35), 85–106. [In Persian]
5. Barzizza, E., Campbell, S., Ceccato, R., Dobosz, A., Haag, M., Martins, R., & Salmaso, L. (2024). A data-driven approach to understanding customer satisfaction. Journal of Machine Intelligence and Data Science, 5. https://doi.org/10.11159/jmids.2024.001
6. Benaddi, L., Ouaddi, C., Jakimi, A., & Ouchao, B. (2024). A systematic review of chatbots: Classification, development, and their impact on tourism. IEEE Access, 12, 78799–78810. https://doi.org/10.1109/ACCESS.2024.3408108
7. Bhuiyan, M., Rahman, M. A., Hoque, A. B., Ashrafuzzaman, M., & Rahman, A. (2023). Advanced analytics and machine learning for revenue optimization in the hospitality industry: A comprehensive review of frameworks. American Journal of Scholarly Research and Innovation, 2(2), 52–74. https://doi.org/10.63125/8xbkma40
8. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., & DeNisi, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524
9. Cutler, S. Q., & Carmichael, B. A. (2010). The dimensions of the tourist experience. In M. Morgan, P. Lugosi, & J. R. B. Ritchie (Eds.), The tourism and leisure experience (pp. 3–26). Multilingual Matters. https://doi.org/10.21832/9781845411503-004
10. Diwan, S. A. (2025). Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT. Alexandria Engineering Journal, 116, 535–547. https://doi.org/10.1016/j.aej.2024.11.051
11. Elhabyb, K., Baina, A., Bellafkih, M., & Deifalla, A. F. (2024). Machine learning algorithms for predicting energy consumption in educational buildings. International Journal of Energy Research, 2024(1), Article 6812425. https://doi.org/10.1155/2024/6812425
12. Fallah Tafti, H. (2020). Predicting the number of tourists based on Google Trends data records using machine learning methods: A case study of tourists in Yazd. Journal of Tourism and Development, 10(2), 67–79. [In Persian]
13. Fouad, A. M., Salem, I. E., & Fathy, E. A. (2024). Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap. Tourism and Hospitality Research. Advance online publication. https://doi.org/10.1177/14673584241293125
14. Garner, B., Thornton, C., Luo Pawluk, A., Mora Cortez, R., Johnston, W., & Ayala, C. (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. Journal of Business Research, 139, 1366–1377. https://doi.org/10.1016/j.jbusres.2021.08.025
15. Iloranta, R., & Komppula, R. (2022). Service providers' perspective on the luxury tourist experience as a product. Scandinavian Journal of Hospitality and Tourism, 22(1), 39–57. https://doi.org/10.1080/15022250.2021.1946845
16. Lundberg, E. (2017). The importance of tourism impacts for different local resident groups: A case study of a Swedish seaside destination. Journal of Destination Marketing & Management, 6(1), 46–55. https://doi.org/10.1016/j.jdmm.2016.02.002
17. Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
18. Mehra, P. (2023). Unexpected surprise: Emotion analysis and aspect-based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists. Tourism Management Perspectives, 45, Article 101063. https://doi.org/10.1016/j.tmp.2022.101063
19. Mirthipati, T. (2024). Enhancing airline customer satisfaction: A machine learning and causal analysis approach (arXiv preprint arXiv:2405.09076). https://doi.org/10.48550/arXiv.2405.09076
20. Nam, S., Yoon, S., Raghavan, N., & Park, H. (2021). Identifying service opportunities based on outcome-driven innovation framework and deep learning: A case study of hotel service. Sustainability, 13(1), Article 391. https://doi.org/10.3390/su13010391
21. Prayag, G., Hosany, S., & Odeh, K. (2013). The role of tourists' emotional experiences and satisfaction in understanding behavioral intentions. Journal of Destination Marketing & Management, 2(2), 118–127. https://doi.org/10.1016/j.jdmm.2013.05.001
22. Ren, H., Pang, B., Bai, P., Zhao, G., Liu, S., Liu, Y., & Li, M. (2024). Flood susceptibility assessment with random sampling strategy in ensemble learning (RF and XGBoost). Remote Sensing, 16(2), Article 320. https://doi.org/10.3390/rs16020320
23. Sahebi, A. G., Kordheydari, R., & Aghaei, M. (2022). A new approach in marketing research: Identifying the customer expected value through machine learning and big data analysis in the tourism industry. Asia-Pacific Journal of Management and Technology, 2(3). https://doi.org/10.46977/apjmt.2022v02i03.004
24. Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/BJML/2024/007
25. Sharma, R., Kumar, A., & Chuah, C. (2021). Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer. International Journal of Information Management Data Insights, 1(2), Article 100050. https://doi.org/10.1016/j.jjimei.2021.100050
26. Shrestha, D., Wenan, T., Shrestha, D., Rajkarnikar, N., & Jeong, S.-R. (2024). Personalized tourist recommender system: A data-driven and machine-learning approach. Computation, 12(3), Article 59. https://doi.org/10.3390/computation12030059
27. Sun, H., Kim, M., Kim, S., & Choi, L. (2025). A methodological exploration of generative artificial intelligence (AI) for efficient qualitative analysis on hotel guests' delightful experiences. International Journal of Hospitality Management, 124, Article 103974. https://doi.org/10.1016/j.ijhm.2024.103974
28. Tan, J., Zhao, Z., Ma, W., Liu, Y., & Zhao, H. (2025). Price fairness perception on online food service platforms: A data-driven approach using fsQCA and machine learning. International Journal of Hospitality Management, 125, Article 104014. https://doi.org/10.1016/j.ijhm.2024.104014
29. Van Leeuwen, R., & Koole, G. (2022). Data-driven market segmentation in hospitality using unsupervised machine learning. Machine Learning with Applications, 10, Article 100414. https://doi.org/10.1016/j.mlwa.2022.100414
30. Veréb, V., & Azevedo, A. (2019). A quasi-experiment to map innovation perception and pinpoint innovation opportunities along the tourism experience journey. Journal of Hospitality and Tourism Management, 41, 208–218. https://doi.org/10.1016/j.jhtm.2019.10.003
31. Wang, J., Zhao, Z., Liu, Y., & Guo, Y. (2021). Research on the role of influencing factors on hotel customer satisfaction based on BP neural network and text mining. Information, 12(3), Article 99. https://doi.org/10.3390/info12030099
32. Wang, Y., Tang, T., Zhang, W., Sun, Z., & Xiong, Q. (2021). The Achilles tendon of dynamic pricing: The effect of consumers' fairness preferences on platform's dynamic pricing strategies. Journal of Internet and Digital Economics, 1(1), 15–35.
33. Wu, W., & Riantama, D. (2022). Determining the factors affecting customer satisfaction using an extraction-based feature selection approach. PeerJ Computer Science, 8, Article e850. https://doi.org/10.7717/peerj-cs.850
34. Wüst, K., & Bremser, K. (2025). Artificial intelligence in tourism through chatbot support in the booking process—An experimental investigation. Tourism and Hospitality, 6(1), Article 36. https://doi.org/10.3390/tourhosp6010036
35. Zhang, X., & Kim, H.-S. (2021). Customer experience and satisfaction of Disneyland hotel through big data analysis of online customer reviews. Sustainability, 13(22), Article 12699. https://doi.org/10.3390/su132212699