Fake Review Detection Using Transformer-Based Approach on Amazon Dataset

Shukla, Swati, Stynes, Paul and Sahni, Vikas (2025) Fake Review Detection Using Transformer-Based Approach on Amazon Dataset. In: 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, Paris, France. ISBN 979-8-3315-3559-9

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Abstract

The rapid expansion of e-commerce platforms has made online reviews a critical factor in shaping consumer trust and influencing their purchasing decisions. However, with the increasing growth, there is also an increase in the occurrences of fake reviews, which has become a significant concern, leading to misinformation and loss of customer trust. This project aims to address the challenge of fake review detection by leveraging the power of Bidirectional Encoder Representation from Transformers (BERT), a transformer-based language model. By capturing the contextual and semantic nuances of review text, BERT significantly improves the accuracy of identifying deceptive patterns that are often overlooked by traditional models. The use of BERT in this domain ensures a more robust and reliable detection system, capable of distinguishing between authentic and fake reviews with high precision. This contributes to building transparency and reliability in digital marketplaces (e-commerce), enhancing both user trust and platform credibility.

Item Type:	Book Section
Uncontrolled Keywords:	Authenticity; BERT; e-commerce; Fake review detection; natural language processing; text classification
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HF Commerce > Electronic Commerce
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	13 May 2026 10:41
Last Modified:	13 May 2026 11:10
URI:	https://norma.ncirl.ie/id/eprint/9303

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