Fake Product Review Detection

This repository contains a machine learning project aimed at detecting and removing fake product reviews using advanced techniques like Sentiment Analysis and Opinion Mining. The system ensures genuine reviews, aiding customers in making informed decisions and helping manufacturers maintain trust and credibility.

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📖 Overview

Fake reviews often distort the reputation of products, misleading customers and causing financial loss. This project identifies and removes deceptive reviews using machine learning techniques. By leveraging Sentiment Analysis, Opinion Mining, and Data Mining, the system supports both customers and businesses by enhancing review authenticity.

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📌 Table of Contents

• Introduction
• Features
• Project Structure
• Technologies Used
• Installation
• Usage
• Results
• Future Enhancements
• Contributing
• License
• References

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Introduction

In the digital era, online reviews play a crucial role in shaping product reputations and driving sales. However, fake reviews are intentionally written to mislead consumers. This project implements a Fake Review Detection System that:

• Identifies fake reviews based on patterns and IP addresses.
• Uses machine learning techniques for analysis and detection.
• Removes fake reviews to ensure genuine feedback is available to users.

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✨ Features

Fake Review Detection: Identifies deceptive reviews using sentiment analysis and opinion mining. Review Management: Automatically flags and removes fake reviews. Insights: Provides detailed analysis to manufacturers and admins for decision-making. User Safety: Protects customers from misleading information and financial losses.

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📂 Project Structure

fake-product-review-detection/
├── README.md                 # Project documentation
├── LICENSE                   # License for the project
├── requirements.txt          # Python dependencies
├── data/                     # Dataset files
│   ├── reviews.csv           # Raw review dataset
├── notebooks/                # Jupyter notebooks
│   ├── FakeReviewDetection.ipynb
├── src/                      # Source code files
│   ├── data_preprocessing.py
│   ├── review_analysis.py
│   ├── review_classification.py
├── images/                   # Visualization images
│   ├── fake_review_distribution.png
│   ├── feature_importance.png
├── docs/                     # Documentation files
│   ├── project_report.pdf
│   ├── references.md

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🛠️ Technologies Used

• Programming Language: Python 3.8+
• Libraries:
  • Scikit-learn
  • NLTK
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
• Development Environment: Jupyter Notebook, Google Colab

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📥 Installation

Prerequisites

• Python 3.8+
• Libraries: scikit-learn, nltk, pandas, matplotlib, seaborn

Steps

1. Clone the repository:

   git clone https://github.com/<adithi741>/fake-product-review-detection.git

2. Navigate to the directory:

   cd fake-product-review-detection

3. Install dependencies:

   pip install -r requirements.txt

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🚀 Usage

1. Prepare the Dataset

• Place your dataset in the data/ folder.
• Ensure the file is named reviews.csv.

2. Run the Notebooks

• Open and execute the notebooks under notebooks/ in Jupyter or Colab:
• FakeReviewDetection.ipynb: Perform end-to-end detection and analysis.

3. Execute Scripts

• Use Python scripts for automation:

  python src/data_preprocessing.py
  python src/review_analysis.py
  python src/review_classification.py

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📊 Results

• Accuracy: Achieved 90% detection accuracy.
• Key Features Identified: 1. Review Text Sentiments 2. IP Address Patterns 3. Frequency of Similar Reviews
• Visualizations: Insights are saved in the images/ folder.

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🔮 Future Enhancements

• Implement deep learning models for improved detection accuracy.
• Expand analysis to include multilingual reviews.
• Deploy the system on a cloud platform for real-time monitoring.

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🤝 Contributing

Contributions are welcome! Fork the repository, create a feature branch, and submit a pull request.

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📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

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📚 References

1. Fake Product Review Detection Using Machine Learning
2. IEEE Xplore
3. IJRASET Research Paper
4. GitHub Topics: Fake Review Detection
5. Opinion Mining on ScienceDirect