Food Recipes Rating System based on Sentiment analysis
Amit Pandit
Project Manager
Web Developer
AI Developer
Django
scikit-learn
SQLite
# Food Recipes Rating System Based on Sentiment Analysis
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Tech Stack](#tech-stack)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This project is a Food Recipes Rating System that utilizes sentiment analysis to rate reviews. Reviews are analyzed using an LSTM model to determine their sentiment, which then translates into a numerical rating from 1 to 5. The system is built using Django for the backend, and it incorporates NLTK and Scikit-learn for natural language processing and machine learning.
## Features
- User authentication using GitHub OAuth
- Recipe posting with detailed descriptions and images
- Review submission with sentiment-based rating (1 to 5)
- Sentiment analysis using a custom LSTM model
- Email notifications through Google API
## Tech Stack
- **Backend:** Django
- **Natural Language Processing:** NLTK, Scikit-learn
- **Machine Learning Model:** LSTM (Long Short-Term Memory)
- **Authentication:** GitHub OAuth
- **Email Service:** Google API
- **Database:** SQLite (default Django setup)
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/food-recipes-rating-system.git
cd food-recipes-rating-system
```
2. **Create a virtual environment and activate it:**
```bash
python3 -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
4. **Apply the migrations:**
```bash
python manage.py migrate
```
5. **Run the development server:**
```bash
python manage.py runserver
```
## Usage
1. **Navigate to the homepage:**
Open your web browser and go to `http://127.0.0.1:8000/`.
2. **Sign in using GitHub:**
Use GitHub OAuth to authenticate and access the platform.
3. **Post a recipe:**
Create a new recipe by filling out the form with the required details.
4. **Submit a review:**
Leave a review for any recipe. The sentiment of your review will be analyzed, and a rating from 1 to 5 will be assigned based on the sentiment score.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
