Surfacing hidden signal in 23,000 product reviews

Wine importers had no systematic view of how their portfolio was performing in language

SAQ product reviews represent one of the most data-rich, publicly available signals in the Quebec wine market. Yet importers were making portfolio and sales decisions without any systematic analysis of what reviewers were actually saying - and crucially, how their products compared linguistically to others in the same agency tier.

The gap wasn't access to data. It was the absence of a pipeline to make that data legible at scale.

From raw review text to structured competitive intelligence

All 23,000+ publicly available SAQ product reviews were ingested, cleaned, and segmented by import agency. A sentiment delta model was applied to surface products where reviewer language diverged most sharply - identifying not just negative reviews, but the specific linguistic patterns that predicted negative reception.

Output: per-agency scorecards identifying products with the most distinct and consistent negative language signals - ranked, annotated, and ready for immediate commercial use.

Language patterns that sales data alone would never surface

Behavioral language context, applied to commercial data

This analysis was informed by six years of embedded work in behavioral language analytics - understanding not just sentiment polarity, but the specific linguistic structures that signal trust, disappointment, surprise, and confidence in product reviews. That context shapes how pipelines are designed and how outputs are interpreted.

The result is analysis that goes beyond a sentiment score to surface what language is actually doing in the text - and what it means for the business decision at hand.