  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.
