PDF(Pubmed)
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.
摘要：
大社交数据和用户生成的内容已经成为及时和丰富的知识的重要来源，以检测客户的行为模式。通过使用用户生成的内容来揭示客户满意度一直是商业中的一个重要问题，特别是在旅游和酒店方面。有许多关于客户满意度的研究采取了定量调查的方法。然而,利用社交大数据以eWOM（电子口碑）的形式揭示顾客满意度可以成为更好地了解顾客需求的有效途径。在这项研究中,我们的目标是开发一种基于监督学习的混合方法，文本挖掘,和细分机器学习方法，用于分析麦加酒店旅行者决策的大社会数据，沙特阿拉伯。要做到这一点，我们使用序列最小优化(SMO)的支持向量回归，潜在狄利克雷分配(LDA)，和k-均值方法来开发混合方法。我们从TripAdvisor上的麦加酒店的旅行者在线评论中收集数据。数据是分段的，旅行者的满意度是根据他们对酒店的在线评论显示的。结果表明,该方法对麦加酒店的社交大数据分析和旅行者细分是有效的。对结果进行了讨论，并为酒店管理者提供了一些建议和策略，以提高他们的服务质量和提高客户满意度。
