论文标题
关于使用在线评论的机器学习应用程序的系统文献综述
A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews
论文作者
论文摘要
消费者情感分析是最近针对社交媒体相关应用的FAD，例如医疗保健，犯罪，金融，旅行和学者。解开消费者的看法，以深入了解所需的目标，并进行评论很重要。随着技术的发展，大量社交网络数据在数量，主观性和异质性方面增加，手动处理它变得挑战。机器学习技术已被用来处理现实生活中的困难。本文介绍了这项研究，以找出这种机器学习技术联盟对消费者情感分析的有用性，范围和适用性，以分析酒店和旅游业领域的在线评论。我们已经展示了一项系统的文献综述，以适当的方式比较，分析，探索和理解尝试和方向，以找到研究差距来说明这种配对的未来范围。这项工作以两种方式为现有文献做出了贡献。首先，主要目的是阅读和分析机器学习技术的使用，以分析用于在线审查和旅游业领域的在线评论。其次，在这项工作中，我们提出了一种系统的方法，以识别，收集观察证据，分析结果以及对所有相关高质量研究的观察结果，以解决指代所述研究领域的特定研究查询。
Consumer sentiment analysis is a recent fad for social media related applications such as healthcare, crime, finance, travel, and academics. 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 terms of volume, subjectivity, and heterogeneity, becomes challenging to process it manually. Machine learning techniques have been utilized to handle this difficulty in real-life applications. This paper presents the study to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. We have shown a systematic literature review to compare, analyze, explore, and understand the attempts and direction in a proper way to find research gaps to illustrating the future scope of this pairing. This work is contributing to the extant literature in two ways; firstly, the primary objective is to read and analyze the use of machine learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. Secondly, in this work, we presented a systematic approach to identify, collect observational evidence, results from the analysis, and assimilate observations of all related high-quality research to address particular research queries referring to the described research area.
