Engineering a Privacy-First Emotion Analytics Pipeline for Regulated Healthcare Data

Introduction: The engineering problem Briefly restate the challenge (unstructured healthcare feedback) Emphasise engineering constraints, not product vision Reference regulated environments Why privacy must come before modelling Why PII redaction must happen before storage Trade-offs: recall vs safety Why post-hoc anonymisation is insufficient Designing the emotion analytics pipeline Multi-label emotion detection Handling overlapping emotional states Calibration and confidence thresholds Topic and trend analysis at scale Why individual documents are noisy Rolling windows (7/30/90 days) Avoiding false positives Rule-plus-ML decision logic Why pure ML fails in regulated settings Deterministic rules + probabilistic signals Interpretability benefits Explainability as an engineering requirement Evidence selection Rationale generation Model versioning Lessons from early builds What broke What surprised you What you would redesign Conclusion Engineering mindset over hype Decision support, not