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

Abstract

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.