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

Content Provider	Europe PMC
Author	Alsayat, Ahmed
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.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9616417&blobtype=pdf
ISSN	09410643
Journal	Neural Computing & Applications [Neural Comput Appl]
Volume Number	35
DOI	10.1007/s00521-022-07992-x
PubMed Central reference number	PMC9616417
Issue Number	6
PubMed reference number	36340596
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2022-10-28
Publisher Place	London
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Subject Keyword	Text mining Machine learning Big social data eWOM Customer satisfaction Segmentation Hotel industry Customer decision-making
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software