Research Trends on Sentiment Analysis and Imbalanced Data Handling in Fake Review Detection: A Systematic Literature Review
DOI:
https://doi.org/10.21015/vtse.v14i2.2328Abstract
Fake reviews are deceptive evaluations that mislead customers rather than reflect genuine customer experiences. These reviews can damage the business's reputation by deceiving the customers, which then causes them to make poor decisions about what to buy and diminishes the trust that e-commerce platforms can have. Detecting fake reviews is crucial for e-commerce platforms to maintain their integrity, protect consumers, and uphold business reputations. Despite its importance, there is a paucity of comprehensive research addressing fake review detection through the lenses of Sentiment Analysis (SA) and imbalanced data handling. To bridge this gap, a systematic literature review uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review analyzed 43 studies from the Scopus and Web of Science databases, covering the period from 2019 to 2024. Three primary themes emerged: SA levels, detection methods, and techniques for handling imbalanced data, which further branched into 28 sub-themes. The analysis revealed key trends such as a predominant focus on document-level SA, the application of machine learning approaches, and data resampling techniques to address imbalanced datasets. The review underscored the necessity for more research on aspect-level analysis and the development of combinational approaches, such as hybrid models, to enhance the accuracy and reliability of fake review detection. These insights provide valuable guidance for researchers, data scientists, and developers seeking to advance the field.
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