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

TL;DR

This paper systematically reviews how machine learning techniques are applied to analyze consumer sentiment from online reviews specifically in the hospitality and tourism sectors, highlighting research gaps and future directions.

Contribution

It provides a comprehensive analysis of machine learning applications in sentiment analysis for online reviews within hospitality and tourism, identifying research gaps and proposing future research directions.

Findings

Machine learning techniques are effectively used for sentiment analysis in hospitality and tourism.

The review identifies key research gaps and challenges in current methodologies.

Future research directions are proposed to enhance sentiment analysis accuracy.

Abstract

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.