The problem
Why this system needed to exist.
Community chat contains fast, candid product feedback, but reading it message by message does not scale. Raw message counts also fail to explain which themes are changing or how different groups experience the product.
The system needed to create useful product signals without turning model-generated personas into claims about real individuals or exposing direct identifiers.
The system
How the pieces work together.
The sentiment path ingests MongoDB or Delta chat, cleans messages, fine-tunes and registers a three-class DistilBERT model, scores messages in batches, and aggregates daily and topic-level trends.
The persona path redacts light PII, uses secret-scoped pseudonymous identifiers, summarizes recent conversations, generates embeddings, clusters aggregate behavior, and produces structured persona cards and a clearly labeled research-Q&A simulation.