Fake Product Review Detection in E-Commerce using Machine Learning

Abstract

Abstract: In the current era of online shopping, user-generated reviews play a vital role in influencing consumer behavior and decision-making. However, the presence of fake or deceptive reviews severely impacts product reliability and brand reputation. This paper proposes a hybrid deep learning framework that combines Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) models to detect fake reviews effectively. The proposed model utilizes textual, behavioral, and product-based features to capture both semantic and contextual information from user reviews. The dataset undergoes text preprocessing, feature extraction, and tokenization before training the classification models. The LSTM network handles sequential dependencies in the text, while XGBoost strengthens prediction accuracy through ensemble learning. The hybrid system achieves superior accuracy, outperforming traditional models and providing a robust mechanism for identifying deceptive online reviews.

Introduction

The rapid growth of e-commerce platforms like Amazon, Flipkart, and Yelp has made online product reviews a critical factor influencing customer purchasing decisions. While reviews guide consumer trust and brand reputation, they are often manipulated through fake or deceptive content, challenging the authenticity of e-commerce ecosystems. Detecting such fake reviews is difficult because they mimic genuine writing styles and emotions. Traditional machine learning methods using hand-crafted features (e.g., Bag-of-Words, TF-IDF) fail to capture deeper semantic meaning, limiting their effectiveness.

Recent advances in Natural Language Processing (NLP) and Deep Learning, particularly Long Short-Term Memory (LSTM) networks, allow for contextual and sequential analysis of reviews, detecting linguistic cues of deception. However, LSTMs underutilize structured metadata like review timestamps or user activity. To address this, XGBoost, a gradient boosting ensemble method, complements LSTM by analyzing statistical and tabular features.

The proposed hybrid LSTM-XGBoost model integrates the semantic understanding of LSTM with the feature-based precision of XGBoost. LSTM captures emotional and linguistic patterns, while XGBoost handles structured review features. Their combined predictions improve accuracy, generalization, and robustness in identifying fake reviews, supporting trustworthy e-commerce platforms.

Methodology & System Design:

1. Data Collection & Preprocessing: Reviews are collected from platforms like Amazon and Yelp, cleaned (lowercasing, punctuation removal, lemmatization, stopword removal), tokenized, and balanced for class distribution.

2. Feature Extraction: TF-IDF vectors are used for XGBoost, and word embeddings for LSTM, ensuring both statistical and semantic features are captured.

3. Model Training: LSTM learns sequential patterns and emotional tone; XGBoost detects statistical irregularities in features.

4. Hybrid Classification: Predictions from both models are fused to classify reviews as genuine or fake.

5. System Architecture: Modular design ensures scalability, efficient data flow, and user-friendly outputs, allowing easy integration of new models or datasets.

Conclusion

The Fake Review Detection System using LSTM and XGBoost was developed to effectively identify deceptive and misleading reviews on e-commerce platforms. The system successfully integrates the strengths of deep learning and machine learning algorithms to provide accurate classification of reviews as genuine or fake. By analyzing textual content, sentiment, and contextual patterns, the model enhances transparency and helps in improving user trust in online marketplaces.The hybrid approach of combining LSTM, which captures contextual dependencies, and XGBoost, which focuses on numerical and structured feature learning, resulted in superior performance compared to traditional models. Through rigorous evaluation, the system achieved an accuracy of 94.2%, demonstrating its reliability and robustness for large-scale review datasets. The project thus validates the effectiveness of hybrid learning for natural language-based classification problems. Overall, the proposed system provides a practical and scalable solution for detecting fake reviews. It can serve as a valuable tool for e-commerce platforms to maintain integrity, improve decision-making, and enhance user confidence. The success of this project highlights how artificial intelligence and data science can contribute to solving real-world problems in digital communication and consumer analytics.

References

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Copyright

Copyright © 2025 U. Nithya, P. Roobavani, D. Pravin Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.