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Consumer sentiment analysis is a recent fad for social media relatedapplications such as healthcare, crime, finance, travel, and academics.Disentangling consumer perception to gain insight into the desired objectiveand reviews is significant. With the advancement of technology, a massiveamount of social web-data increasing in terms of volume, subjectivity, andheterogeneity, becomes challenging to process it manually. Machine learningtechniques have been utilized to handle this difficulty in real-lifeapplications. This paper presents the study to find out the usefulness, scope,and applicability of this alliance of Machine Learning techniques for consumersentiment analysis on online reviews in the domain of hospitality and tourism.We have shown a systematic literature review to compare, analyze, explore, andunderstand the attempts and direction in a proper way to find research gaps toillustrating the future scope of this pairing. This work is contributing to theextant literature in two ways; firstly, the primary objective is to read andanalyze the use of machine learning techniques for consumer sentiment analysison online reviews in the domain of hospitality and tourism. Secondly, in thiswork, we presented a systematic approach to identify, collect observationalevidence, results from the analysis, and assimilate observations of all relatedhigh-quality research to address particular research queries referring to thedescribed research area.

A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews