01. Fake Review Detection on Amazon using Deep Neural Network

In the era of e-commerce dominance, an increase in fake reviews on online shopping platforms compromises the integrity of consumer feedback systems. This study focuses on Amazon, a leading e-commerce platform in the United States, where fake reviews have become a significant concern. Given the limited availability of authentic datasets for analysis, we propose a novel methodology to differentiate between genuine and fraudulent reviews across verified and non-verified purchases. Our approach utilizes the bootstrap distribution of cosine similarity values, providing a robust statistical foundation for review classification. We present a comprehensive framework integrating Convolutional Neural Networks with word embedding and emotion-mining techniques through Natural Language Processing. Our method demonstrates exceptional performance, achieving an accuracy rate of over 96% in distinguishing fake reviews from user reviews. This research aims to foster trust in online marketplaces and protect consumers from misleading information by providing a powerful tool for fake review detection.

Convolutional Neural Network

Bootstrap

Natural Language Processing 

Mid-Level

Community

Women in Statistics and Data Science 2025