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https://github.com/the-developer-306/fake-review-detector
This project is a machine learning-based review classification system that predicts whether a product review is GENUINE or FAKE. It preprocesses review text, analyzes sentiment, and uses numerical features like ratings and helpfulness to make predictions. The model is deployed via a Flask web application for user interaction.
https://github.com/the-developer-306/fake-review-detector
classification flask logistic-regression machine-learning numpy pandas python renderdeploy scikit-learn sentiment-analysis
Last synced: 21 days ago
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This project is a machine learning-based review classification system that predicts whether a product review is GENUINE or FAKE. It preprocesses review text, analyzes sentiment, and uses numerical features like ratings and helpfulness to make predictions. The model is deployed via a Flask web application for user interaction.
- Host: GitHub
- URL: https://github.com/the-developer-306/fake-review-detector
- Owner: the-developer-306
- Created: 2024-12-19T16:49:24.000Z (28 days ago)
- Default Branch: main
- Last Pushed: 2024-12-23T18:35:26.000Z (23 days ago)
- Last Synced: 2024-12-23T19:29:54.264Z (23 days ago)
- Topics: classification, flask, logistic-regression, machine-learning, numpy, pandas, python, renderdeploy, scikit-learn, sentiment-analysis
- Language: HTML
- Homepage: https://fake-review-detector-jhns.onrender.com
- Size: 26.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Fake Review Detector
This project leverages machine learning to detect fake reviews, providing a reliable tool for identifying genuine and fraudulent reviews. The Fake Review Detector analyzes various features of reviews, such as text sentiment, review length, helpful votes ratio, and more, to classify reviews as either "FAKE" or "GENUINE."
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Model](#model)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
The Fake Review Detector project aims to enhance the credibility of online reviews by distinguishing between genuine and fake reviews. By preprocessing the review text and using a trained classifier, the project provides an effective solution to identify fraudulent reviews.
## Features
- Data preprocessing and cleaning
- Feature extraction (review length, word count, sentiment analysis, etc.)
- Model training and evaluation
- Fake review detection
## Installation
Clone the repository and install the necessary dependencies:
```
git clone https://github.com/the-developer-306/Fake-Review-Detector.git
cd Fake-Review-Detector
pip install -r requirements.txt
```
## Usage
1. **Run the Application**:
- Use the following command to start the Flask app:
```
python app.py
```
## Model
The project employs several machine learning techniques to classify reviews. The models explored include:
- **Logistic Regression**: A statistical method to predict the probability of a binary outcome.
- **Vectorization Pipeline**: Transforming text data into numerical format using TF-IDF.
- **Sentiment Analysis**: Using VADER sentiment analysis to extract sentiment scores.
Performance is evaluated using metrics such as accuracy, precision, and recall.
## Contributing
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License.
## Contact
For any questions or suggestions, feel free to reach out:
- GitHub: [the-developer-306](https://github.com/the-developer-306)
- Email: [[email protected]](mailto:[email protected])