International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In the last few years, fake customer reviews have become a big problem for companies and consumers in online shopping websites. The history of this project started when many shopkeepers complained about fake negative reviews hurting their business. When customers buy products based on fake reviews, they feel cheated and loose trust in online shopping platforms. This problem statement shows the need to find better ways to find fake reviews and remove them from websites. The solution is to use AI technology like BERT models that can understand language patterns in reviews and identify which ones are fake. By combining BERT with other methods like CNN and capsule networks, the accuracy of detecting fake reviews improves a lot. The system will look at things like writing style, emotional words, and unusual patterns that might show fake reviews. Tests showed that our method finds fake reviews with 92% accuracy which is better than older methods. This project will help make online shopping more trustworthy for everyone and protect honest businesses from getting bad reviews that are not real.

Keywords: Fake reviews detection, BERT models, sentiment analysis, customer feedback, machine learning, neural networks, e-commerce platforms, text classification, data augmentation, transformer models

Downloads: | DOI: 10.17148/IJARCCE.2025.14939