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. 2023;35(6):4701-4722.
doi: 10.1007/s00521-022-07992-x. Epub 2022 Oct 28.

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Ahmed Alsayat  1
Affiliations

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Ahmed Alsayat. Neural Comput Appl. 2023.

Abstract

Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers' behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers' demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers' decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k-means approaches to develop the hybrid method. We collect data from travelers' online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers' satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction.

Keywords: Big social data; Customer decision-making; Customer satisfaction; Hotel industry; Machine learning; Segmentation; Text mining; eWOM.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflicts of interest.

Figures

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Saudi Arabia's tourism revenue from 2004 to 2020
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Research method
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K-means procedure
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The graphical representation of LDA
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Travelers’ reviews of Mecca hotels on TripAdvisor
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Segment visualization
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Top 15 satisfaction dimensions for the six segments
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Top 15 satisfaction dimensions for the six segments
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The MAE and RMSE in 150 trials using the training set
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R2 results for the six segments
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Computation time of different methods
See this image and copyright information in PMC

References

    1. Halim YT, Halim HT (2013) Guest Satisfaction and Hotel Profitability in Egypt. Journal of Association of Arab Universities for Tourism and Hospitality, 10(1).
    1. Laškarin Ažić M, Dlačić J, Suštar N. Loyalty trends and issues in tourism research. Tourism and Hospitality Manag. 2020;26(1):133–155. doi: 10.20867/thm.26.1.8. - DOI
    1. Chang Y-C, Ku C-H, Chen C-H. Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. Int J Inf Manage. 2019;48:263–279. doi: 10.1016/j.ijinfomgt.2017.11.001. - DOI
    1. Tran LTT, Ly PTM, Le LT. Hotel choice: a closer look at demographics and online ratings. Int J Hosp Manag. 2019;82:13–21. doi: 10.1016/j.ijhm.2019.03.015. - DOI
    1. Barsky JD. Customer satisfaction in the hotel industry: Meaning and measurement. Hospitality Res J. 1992;16(1):51–73. doi: 10.1177/109634809201600105. - DOI

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