Consumer sentiment analysis is a recent fad for social media-related applications such as healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to gain insight into the desired objective and reviews is significant. With the advancement of technology, a massive amount of social web data increasing in volume, subjectivity, and heterogeneity becomes challenging to process manually. Machine learning (ML) techniques have been utilized to handle this difficulty in real-life applications. This paper presents a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism. We show a systematic literature review to compare, analyse, explore, and understand the attempts and directions to find research gaps in illustrating the future scope of this pairing. The primary objective is to read and analyse the use of ML techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. This research has significant implications for service providers in terms of developing managerial strategies for consumers in terms of selecting services that meet their needs. Furthermore, there is high impact for researchers in terms of prospective research directions.

消费者情绪分析是社交媒体相关应用（例如医疗保健、犯罪、金融、旅行和学术界）的最新时尚。理清消费者的认知以深入了解所需的目标和评论非常重要。随着技术的进步，海量社交网络数据在数量、主观性和异构性方面的增加变得难以手动处理。机器学习 (ML) 技术已被用于解决现实生活应用中的这一难题。本文提出了一项研究，以确定这种 ML 技术联盟在酒店和旅游领域的在线评论中进行消费者情绪分析 (CSA) 的有用性、范围和适用性。我们展示了系统的文献综述来比较、分析、探索、并了解在说明这种配对的未来范围时寻找研究差距的尝试和方向。主要目标是阅读和分析 ML 技术在酒店和旅游领域在线评论的消费者情绪分析中的使用。这项研究对服务提供者在为消费者选择满足其需求的服务方面制定管理策略方面具有重要意义。此外，在前瞻性研究方向方面对研究人员有很大影响。这项研究对服务提供者在为消费者选择满足其需求的服务方面制定管理策略方面具有重要意义。此外，在前瞻性研究方向方面对研究人员有很大影响。这项研究对服务提供者在为消费者选择满足其需求的服务方面制定管理策略方面具有重要意义。此外，在前瞻性研究方向方面对研究人员有很大影响。