AI Insights - Sentiment Analysis

AI Insights is a project aimed at measuring public sentiment towards AI topics using Twitter as the dataset. The project leverages a microservices architecture to collect, analyze, and display sentiment data.

Features

• Microservices Architecture: Decoupled services for data collection, analysis, and presentation.
• Message Queue: Uses RabbitMQ to handle communication between services.
• CI/CD Pipeline: Automated deployment using GitHub Actions.
• Containerization: Services are containerized using Docker and orchestrated with Docker Compose.
• Data Visualization: Utilizes D3.js for interactive charts and visualizations.

Technologies Used

• Python: The primary programming language.
• Flask: For building the web service and API.
• SQLAlchemy: ORM for database interaction.
• Flask-Smorest: For creating REST APIs.
• Marshmallow: For object serialization and deserialization.
• D3.js: For data visualization.
• RabbitMQ: For message queuing between services.
• Docker: To containerize the application.
• Docker Compose: For container orchestration.
• GitHub Actions: For continuous integration and deployment.
• AWS EC2: Hosting the application.
• Google Cloud Natural Language API: For sentiment analysis.
• PostgreSQL: Database for storing tweet data.

Microservices

• Data Collector Service:

  • Reaches out to the Twitter API and pulls in recent tweets related to AI.
  • Sends the tweets to a RabbitMQ message queue.

• Data Analyzer Service:

  • Receives tweets from RabbitMQ.
  • Uses Google Cloud Natural Language Processing service to analyze the sentiment.
  • Persists analyzed tweets to a PostgreSQL database.

• Web Service:

  • A Flask app providing the backend API.
  • Uses Flask, SQLAlchemy, Flask-Smorest, and Marshmallow.
  • Serves static content utilizing D3.js for visualizations.

Production Environment

The services are containerized using Docker, with Docker Compose for orchestration. GitHub Actions handles the CI/CD pipeline, deploying the containerized application to an AWS EC2 Ubuntu instance. This setup ensures seamless updates and scalability.

Live Demo

You can view the live version of the project here.

GitHub Repository

The source code for AI Insights is available on GitHub. Feel free to explore the repository, open issues, or contribute to the project here.

This project demonstrates the effective use of a microservices architecture, message queuing, and modern DevOps practices including CI/CD and containerization.