Sentiment Analysis Project

This project is a sentiment analysis web service built with Django, MySQL, and OpenAI GPT-4o. It allows users to input text and receive real-time sentiment analysis results.

🌟 Demo

Check out the live demo: https://sa.hsingh.site/

📋 Prerequisites

• Docker
• Docker Compose

🚀 Installation

🐳 Docker

1. Clone the repository:

   git clone https://github.com/siinghd/sentiment-analysis.git
   cd sentiment-analysis

2. Configure docker-compose.yml with correct ENV VARS. Look here

3. Build the Docker containers:

   docker-compose build # or docker compose build

4. Start the containers:

   docker-compose up # or docker compose up

   This will start the following services:

   • db: MySQL database container
   • web: Django web application container
   • migrate: Container to run database migrations (run and exit)

5. Access the web application:

   Open your web browser and visit http://localhost:8000 to access the sentiment analysis web service.

🖥️ Non-Docker Setup

If you prefer to run the application without using Docker, follow these steps:

Prerequisites

• Python 3.8 or higher
• MySQL Server
• Pip (Python package installer)

Installation

1. Set up the MySQL Database:

   • Install MySQL on your machine.
   • Create a new database and user for the application.
   • Grant all privileges on your database to the new user.

2. Clone the repository and enter the directory:

   git clone https://github.com/siinghd/sentiment-analysis.git
   cd sentiment-analysis

3. Install Python dependencies:

   • Ensure pip is installed and then run:

   pip install -r requirements.txt

4. Configure the application:

   • Copy the example environment configuration file and modify it with your settings:

   cp .env.example .env

   • Open the .env file and update it with your database credentials and other environment variables such as OPENAI_API_KEY and DJANGO_SECRET_KEY.

5. Perform database migrations:

   python manage.py migrate

6. Run the Django development server:

   python manage.py runserver # or pip install gunicorn and run :  gunicorn sentiment_analysis_project.wsgi:application --bind 127.0.0.1:8000

   This will start the server on http://localhost:8000.

7. Access the web application:

   • Open your web browser and visit http://localhost:8000 to access the sentiment analysis web service.

ℹ️ Additional Notes:

• Ensure that your Python environment is correctly set up and that all dependencies listed in requirements.txt are installed without errors.
• Make sure your MySQL service is running and accessible by the Django application.
• Adjust the .env file as necessary to ensure all environment variables are correctly set for your development environment.

⚙️ Configuration

The project uses environment variables for configuration. You can modify the following variables in the docker-compose.yml file:

• OPENAI_API_KEY: Your OpenAI API key for sentiment analysis. Make sure to replace it with your own API key.
• DJANGO_SECRET_KEY: Secret key for Django. Change this to a secure value in production.
• DB_NAME: Name of the MySQL database.
• DB_USER: MySQL database user.
• DB_PASSWORD: MySQL database password.
• DB_HOST: MySQL database host (set to db for Docker Compose setup).
• DB_PORT: MySQL database port.
• DEBUG: Django DEBUG flag, default: false.

📖 Usage

1. Open your web browser and visit http://localhost:8000.
2. Enter the text you want to analyze in the provided input field.
3. Click the "Analyze Sentiment" button.
4. The sentiment analysis result will be displayed on the page.

🏗️ Project Structure

The project has the following structure:

• api/: Django app containing the API views, models, serializers, and exceptions.
• sentiment_analysis_project/: Django project configuration files.
• templates/: HTML templates directory.
• utils/: Useful utilities
• Dockerfile: Dockerfile for building the web application container.
• docker-compose.yml: Docker Compose configuration file.
• manage.py: Django management script.
• requirements.txt: Python dependencies file.

📦 Dependencies

The project uses the following main dependencies:

• Django: Web framework for building the sentiment analysis web service.
• Django REST Framework: Toolkit for building RESTful APIs in Django.
• MySQL: Database for storing sentiment analysis results.
• OpenAI GPT-4o: API for performing sentiment analysis.
• Gunicorn: WSGI HTTP server for running the Django application.

For a complete list of dependencies, refer to the requirements.txt file.

☁️ AWS Deployment

For small projects, using other hosting services like Hetzner might be more cost-effective. But if choosing AWS, here are some recommended services:

• Amazon EKS: Use EKS for managing Kubernetes containers. It's a scalable and secure environment.
• Amazon ECR: Store Docker images in a fully managed registry using ECR.
• Amazon RDS: Use RDS for databases. It makes setting up and scaling databases easier.
• AWS Elastic Beanstalk: Deploy apps quickly on Beanstalk without having to manage lots of hosting stuff manually.
• Amazon EC2: Run the app on EC2 virtual servers with customizable configurations.

🚀 Scaling & Load Balancing

Elastic Load Balancing (ELB): Use ELB with Auto Scaling Groups to efficiently distribute traffic across multiple servers like EC2 instances. It can automatically scale resources based on demand.