Travellers Segmentation and Choice Prediction through Online Reviews: The Case of Wellingtons Hotels in New Zealand

Ali Ahani, Mehrbakhsh Nilashi, Othman Ibrahim


Customer choice and segmentation through online reviews can help hotels to improve their marketing strategy development. Nevertheless, old-style approaches are unproductive in analysing online data generated by customers because of size, dissimilar proportions and structures of online review data. Therefore, this research aims to develop a method for 5-star hotels segmentation and travellers choice forecast through online reviews analysis using machine learning methods. Assessment of method was directed through the gathering of data from travellers ratings of Wellingtons 5-star hotels on different features in TripAdvisor. Results confirm that the projected hybrid machine learning approaches can be applied as a progressive recommender mediator for 5-star hotel segmentation by applying big data obtained from online social media settings.


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Ahani, A., Nilashi, M., & Ahmadi, H. (2016). Evaluating the barriers of hospital information system implementation using analytic network processes (ANP) method. Journal of Soft Computing and Decision Support Systems, 3(4), 30-38.

Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in spa hotels through tripadvisors online reviews. International Journal of Hospitality Management, 80, 52-77.

Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A.R., Knox, K., Samad, S. and Ibrahim, O., (2019). Revealing customers satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331-343.

Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017a). Firm performance through social customer relationship management: Evidence from small and medium enterprises. Paper presented at the 2017 International Conference on Research and Innovation in Information Systems (ICRIIS).

Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017b). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75, 560-578.

Balli, F., & Tsui, W. H. K. (2016). Tourism demand spillovers between Australia and New Zealand: evidence from the partner countries. Journal of Travel Research, 55(6), 804-812.

Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.

Chen, C.-F., & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28(4), 1115-1122.

Cronin Jr, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218.

Davies, D., & Bouldin, D. (1979). A cluster separation measure, IEEE transactions on patter analysis and machine intelligence. vol. In: PAMI-1.

De Pelsmacker, P., Van Tilburg, S., & Holthof, C. (2018). Digital marketing strategies, online reviews and hotel performance. International Journal of Hospitality Management, 72, 47-55.

Ernst, D., & Dolnicar, S. (2018). How to avoid random market segmentation solutions. Journal of Travel Research, 57(1), 69-82.

Gao, B., Li, X., Liu, S., & Fang, D. (2018). How power distance affects online hotel ratings: the positive moderating roles of hotel chain and reviewers travel experience. Tourism Management, 65, 176-186.

Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.

Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. International journal of service industry management, 7(4), 27-42.

Hsu, T.-K., Tsai, Y.-F., & Wu, H.-H. (2009). The preference analysis for tourist choice of destination: A case study of Taiwan. Tourism Management, 30(2), 288-297.

Jafari-Moghadam, S., Zali, M., & Sanaeepour, H. (2017). Tourism entrepreneurship policy: a hybrid MCDM model combining DEMATEL and ANP (DANP). Decision Science Letters, 6(3), 233-250.

Lu, C., Berchoux, C., Marek, M. W., & Chen, B. (2015). Service quality and customer satisfaction: qualitative research implications for luxury hotels. International Journal of Culture, Tourism and Hospitality Research, 9(2), 168-182.

Mohsin, A., & Lockyer, T. (2010). Customer perceptions of service quality in luxury hotels in New Delhi, India: an exploratory study. International Journal of Contemporary Hospitality Management, 22(2), 160-173.

Morteza, Z., Reza, F. M., Seddiq, M. M., Sharareh, P., & Jamal, G. (2016). Selection of the optimal tourism site using the ANP and fuzzy TOPSIS in the framework of Integrated Coastal Zone Management: A case of Qeshm Island. Ocean & coastal management, 130, 179-187.

Neirotti, P., Raguseo, E., & Paolucci, E. (2016). Are customers reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. International Journal of Information Management, 36(6), 1133-1143.

Nilashi, M., Ahani, A., Esfahani, M.D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., Sharef, N.M. and Akbari, E., (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, 767-783.

Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R., & bin Ibrahim, O. (2016). Determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technological Forecasting and Social Change, 111, 244-264.

Nilashi, M., Bin Ibrahim, O., Mardani, A., Ahani, A., & Jusoh, A. (2018a). A soft computing approach for diabetes disease classification. Health Informatics Journal, 24(4), 379-393.

Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCAANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.

Nilashi, M., Ibrahim, O., & Ahani, A. (2016a). Accuracy improvement for predicting Parkinsons disease progression. Scientific reports, 6, 34181.

Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017a). A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics, 34(4), 133-144.

Nilashi, M., Bagherifard, K., Rahmani, M., & Rafe, V. (2017b). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers & industrial engineering, 109, 357-368.

Nilashi, M., Salahshour, M., Ibrahim, O., Mardani, A., Esfahani, M. D., & Zakuan, N. (2016b). A new method for collaborative filtering recommender systems: the case of yahoo! movies and tripadvisor datasets. Journal of Soft Computing and Decision Support Systems, 3(5), 44-46.

Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., . . . Hassan, O. (2019). Factors Influencing Medical Tourism Adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS Approach. Computers & Industrial Engineering, 106005.

Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., & Sanzogni, L. (2019). Analysis of Travellers Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal of Fuzzy Systems, 21(5), 1367-1378.

Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018b). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.

Oliver, R. L. (2014). Satisfaction: A behavioral perspective on the consumer: A behavioral perspective on the consumer: Routledge.

Pahari, S., Ghosh, D., & Pal, A. (2018). An Online Review-Based Hotel Selection Process Using Intuitionistic Fuzzy TOPSIS Method. In Progress in Computing, Analytics and Networking (pp. 203-214): Springer.

Peng, K.-H., & Tzeng, G.-H. (2019). Exploring heritage tourism performance improvement for making sustainable development strategies using the hybrid-modified MADM model. Current Issues in Tourism, 22(8), 921-947.

Ryan, C. (2002). Tourism and cultural proximity: Examples from New Zealand. Annals of Tourism Research, 29(4), 952-971.

Tsui, K. W. H. (2017). Does a low-cost carrier lead the domestic tourism demand and growth of New Zealand? Tourism Management, 60, 390-403.

Wu, C.-S., Lin, C.-T., & Lee, C. (2010). Optimal marketing strategy: A decision-making with ANP and TOPSIS. International Journal of Production Economics, 127(1), 190-196.

Xia, H., Vu, H. Q., Lan, Q., Law, R., & Li, G. (2019). Identifying hotel competitiveness based on hotel feature ratings. Journal of Hospitality Marketing & Management, 28(1), 81-100.

Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199-210.

Yeoman, I., & McMahon?Beattie, U. (2015). New Zealand's future: the potential for knitting tourism. Journal of Tourism Futures, 1(2), 152-155.

Zavadskas, E. K., Turskis, Z., & Kildien?, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179.

Zhang, H., Gu, C.-l., Gu, L.-w., & Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS & information entropyA case in the Yangtze River Delta of China. Tourism Management, 32(2), 443-451.


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