This project builds a real-time system to extract dominant topics and sentiment streams from Twitch chat logs. Designed to detect mood shifts and evolving discussions in live gaming streams, it combines NLP, clustering, and visualization techniques to reveal hidden structures in noisy user-generated text.
- BERTopic + UMAP + HDBSCAN for topic modeling
- VADER + GPT-based sentiment refinement
- WebSocket-based real-time data ingestion
- Tokenized message segmentation with time slicing
- Handled 10K+ chat lines per minute
- Visualized evolving topic clusters across time
- Used GPT-4 to refine topic summaries and labels
- Applied to high-audience gaming streams with no user ID access
1. Real-time chat log collection via Twitch API
2. Text preprocessing and tokenization
3. Topic modeling using BERTopic
4. Sentiment analysis (VADER baseline + GPT refinement)
5. Temporal segmentation for dynamics
6. Visualization of topic shifts across streams
Message count heatmap of Games and Channels
This one is in fact a 3D interactive scatter plot, here is just a screenshot. Dig into more here if interested: