THE PROBLEM
Why this system existed
Aggregate sentiment is less useful without product attribution, trend monitoring, and a path from signal to action.
RoBERTa · NLP · Product Signals
Customer Sentiment & Product Intelligence
RoBERTa-based sentiment scoring across customer channels with category-level reporting and statistically significant change alerts.
MY ROLESentiment modeling, product attribution, reporting, and integration
SCOPEA daily sentiment pipeline combining transformer classification with product-category attribution and trend monitoring.
EVIDENCEProfessional experience · public-safe summary
89.1%sentiment accuracy
50,000+daily records
3corrective actions
OUTCOME
What changed
Processed 50,000+ records daily at 89.1% accuracy and surfaced signals that led to three product corrective actions.
REFERENCE ARCHITECTURE
Controls around the model
01Classify
02Route
03Retrieve
04Rerank
05Generate
06Validate
07Observe
DECISIONS
Trade-offs considered
Five-class sentiment scale
NER-based product attribution
Rolling-mean anomaly alerts
Automated weekly reporting
FAILURE CASE
What did not work
Lexicon baselines missed domain nuance; a RoBERTa model and category attribution produced more actionable product-level signals.
SECURITY BOUNDARY
Public-safe by design
This case study exposes patterns, not employer architecture. It uses synthetic data, no client identifiers, no internal prompts, no proprietary datasets, and no production endpoints.