关于使用在线评论进行消费者情感分析的机器学习应用的系统性文献综述
A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews
摘要：
消费者情感分析是最近流行的社交媒体相关应用，例如医疗保健、犯罪、金融、旅游和学术。解开消费者的感知，以深入了解所需的目标和评论是重要的。随着技术的进步，大量的社交网络数据在数量、主观性和异构性方面不断增加，人工处理变得具有挑战性。机器学习技术已经被用来处理现实生活应用中的这个困难。本文提出的研究，以找出有用的，范围和适用性的机器学习技术联盟的消费者情感分析在线评论在酒店和旅游领域。我们已经展示了一个系统的文献综述，比较，分析，探索和理解的尝试和方向，以适当的方式找到研究差距，说明未来的范围，这种配对。这项工作是促进现存的文献在两个方面;首先，主要目标是阅读和分析使用机器学习技术的消费者情感分析的在线评论领域的酒店和旅游。其次，在这项工作中，我们提出了一种系统的方法来识别，收集观察证据，分析结果，并吸收所有相关高质量研究的观察结果，以解决涉及所述研究领域的特定研究问题。
英文摘要：
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.
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