This paper aims to use machine learning (ML) algorithm for natural language pre-processing (NLP), text mining (TM), and sentiment analysis (SA) techniques to analyze and examine 45,500 online reviews of customers of 50 global chain hotels from different online review sites. Furthermore, the paper addresses the new business value and experiences that the revolutionary 5G technology can bring to the hotel industry. The research findings revealed that the general review star rating corresponds with the opinion (sentiment) scores for the title and the full substance of the online reviews. The case study’s contextual analysis also uncovered that both fulfilled and disappointed customers have a frequent inclination for five categories: food, stay, rooms, service, and staff. This study contributes both theoretically and practically to the multidisciplinary domains of computer science, information systems, and tourism and discovers hidden patterns in data using visual analytics techniques.

本文旨在使用机器学习 (ML) 算法进行自然语言预处理 (NLP)、文本挖掘 (TM) 和情感分析 (SA) 技术，对来自不同地区的 50 家全球连锁酒店的客户的 45,500 条在线评论进行分析和检查。在线评论网站。此外，本文还讨论了革命性的 5G 技术可以为酒店业带来的新业务价值和新体验。研究结果显示，一般评论星级与标题的意见（情绪）分数和在线评论的全部内容相对应。案例研究的上下文分析还发现，满意和失望的客户都经常倾向于五个类别：食物、住宿、房间、服务和员工。