AI Commodity News Tracker (Intern)

The project focuses on building an AI-powered system that automatically collects and analyzes agricultural commodity news (corn, wheat, soybean, etc.) from global sources. It uses NLP techniques to classify and summarize articles, helping analysts assess sentiment and market impact in real time.

Responsibilities:

• Develop web scraping pipelines to gather news data
• Apply NLP models for summarization and sentiment classification
• Store structured results in a database
• Automate daily updates and build an interactive dashboard to visualize trends by commodity and sentiment
• Create dashboards to present news results in a user-friendly format

Technologies:

• Python & Dagster for data collection and automation
• BeautifulSoup or GoogleNews API for scraping
• HuggingFace or OpenAI GPT for NLP
• SQL for managing and querying databases that store the scraped and processed data
• Streamlit or Power BI for visualization