Sentiment measurement at scale

Problem

Measuring public response across social and media channels at a volume that defeats manual review, without introducing a black box nobody can audit. Off-the-shelf sentiment scores were neither explainable nor stable enough to brief from.

Approach

A multi-stage NLP pipeline was built combining several classification models (sentiment, emotion, and topical layers) with reproducible evaluation at each stage, so results could be explained and re-derived rather than trusted on faith. Model outputs land in the warehouse alongside operational data, making public-response measures reportable with the same tooling as everything else.

Technologies

• Python
• Multi-model NLP
• Sentiment and emotion classification
• Amazon Redshift
• Power BI

Outcome

Public-response measurement at production scale with documented, reproducible methodology.