Real-time sentiment analysis of r/WallStreetBets posts and comments. A BERT model scores the community's mood and a live dashboard tracks how it moves with the market.
- 260k+
- data points collected
- 90%+
- sentiment accuracy
- BERT
- model
#The idea
Retail traders on r/WallStreetBets move markets, but the conversation is far too fast to read by hand. Sentire collects posts and comments as they happen, scores each one with a BERT sentiment model, and turns the noise into a signal you can actually look at.
#What it does
- Data collection: over 260,000 posts and comments collected so far, and still growing.
- Sentiment analysis: BERT-based scoring with over 90% accuracy.
- Live sentiment tracking: a 30-day sentiment trend plus today's top-impact posts, ranked by upvotes and comment volume.
- Interactive dashboards: an intuitive interface with customisable views.
#Stack
The frontend is Svelte + TypeScript, styled with Tailwind CSS and DaisyUI, and deployed on Vercel. The data pipeline and model run in Python, with results stored in SQL.
#What's next
Sentire is in public beta with a free waitlist. On the roadmap: automatic stock-mention detection and classification, RESTful API access to the sentiment data, and a chatbot that can answer questions about the latest data.