Live Sentient

Inspiration

The need for smarter, real-time understanding of global news trends. Desire to help users explore news by emotion, topic, and geography.

What it does

Ingests and enriches news articles using AI. Classifies emotion, summarizes content, and enables semantic/geospatial search. Visualizes news by sentiment, topic, and location in real time.

How we built it

Backend: FastAPI with advanced NLP models for enrichment. Database: MongoDB Atlas for scalable, fast geospatial and semantic queries. Frontend: React/Vite for interactive data visualization and search.

Challenges we ran into

Integrating multiple NLP models efficiently. Handling large-scale, real-time data ingestion and indexing. Designing intuitive, responsive visualizations.

Accomplishments that we're proud of

Seamless real-time news enrichment and search. Robust geospatial and semantic querying. User-friendly, insightful visualizations.

What we learned

Best practices for scalable AI data pipelines. Advanced MongoDB Atlas features for search and indexing. Effective frontend-backend integration for real-time apps.

What's next for Live Sentient

Expand language and region coverage. Add user personalization and alert features. Integrate more advanced analytics and visualization tools.

Built With

• geojson
• github-actions-(ci/cd)
• javascript-frameworks/libraries:-fastapi
• languages:-python
• mongodb-atlas-databases:-mongodb-atlas-apis:-news-data-apis
• nginx
• node.js-platforms/cloud-services:-google-cloud-platform-(gcp)
• openai-api-(for-nlp-tasks)-other-technologies:-docker
• react
• restful
• vite