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

arXiv: https://arxiv.org/abs/2008.10282 · HTML: https://mlsanity.com/paper/2008.10282/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [BITE: Textual Backdoor Attacks with Iterative Trigger Injection](https://mlsanity.com/paper/2205.12700/) (2022)
- [Sentiment Simulation using Generative AI Agents](https://mlsanity.com/paper/2505.22125/) (2025)
- [Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions](https://mlsanity.com/paper/2211.03923/) (2022)
- [Sentiment Analysis in Digital Spaces: An Overview of Reviews](https://mlsanity.com/paper/2310.19687/) (2023)
- [CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis](https://mlsanity.com/paper/2507.14022/) (2025)

## Abstract (arXiv)

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.