DE · Done
AI - Sentiment Hub
Overview
Real-time sentiment analysis dashboard with ETL pipeline for scraping raw news data from News API, focusing on AI, blockchain, finance, Forex, and investment topics. Utilizes BERT and VADER models for sentiment analysis, Python for data processing, React JS for the frontend, FastAPI for the backend, Swagger for API documentation, PostgreSQL and Supabase for data storage, and Render for API deployment.
Challenge
Building an efficient ETL pipeline to scrape and process raw news data from News API, integrating BERT and VADER models for accurate sentiment analysis, and creating a responsive dashboard with seamless API and database integration.
Result
Developed a robust web application with an ETL pipeline for real-time news scraping and sentiment analysis, featuring interactive dashboards, detailed analytics, and comprehensive API documentation for actionable insights.
Key Statistics
News API
Data Source
AI, Blockchain, Finance, Forex, Investment
Topics Analyzed
BERT, VADER
Sentiment Models
Render
Deployment Platform
Technologies
Programming
Frontend
Backend
Database
Deployment
Machine Learning
Gallery
Sentiment Dashboard
Screenshot of the main dashboard displaying real-time sentiment trends for selected topics.
Project Overview
Overview section highlighting the ETL pipeline and sentiment analysis workflow.
Analytics View
Analytics page showing detailed sentiment breakdowns using BERT and VADER models.
API Documentation
Swagger interface for the FastAPI backend, documenting endpoints for sentiment data retrieval.