٢٥ جمادى الأولى ١٤٤٧ هـ
“Even negative reviews use positive words.” — Aithal & Tan, ACL Anthology (2021)
A 2021 study by Aithal & Tan uncovered something counterintuitive: even negative reviews often contain more positive words than negative ones.
It’s a phenomenon called positivity bias, our tendency to soften criticism with polite or optimistic language. People want to be fair, not cruel. So they write things like:
“The team was kind, but no one solved my issue.”
“Loved the product, though shipping was awful.”
“Great staff — but still waiting on my refund.”
Each review sounds friendly, but underneath the politeness lies frustration, disappointment, or churn risk.
The Hidden Impact on Reputation Data
In the restaurant world, this bias distorts reality. Thousands of reviews appear overwhelmingly positive, even when operational cracks are showing.
Traditional reputation management tools miss these signals entirely. They rely on keywords and star averages, taking every review at face value. But the truth is in the tone, context, and intent, not just the words.
Worse yet, unhappy customers often leave 4-star ratings, not 1-star ones. And since traditional software doesn’t investigate a +4-star review, critical feedback gets buried under the illusion of satisfaction.
How Sira unleash your operations reality
That’s where Sira’s AI steps in. It doesn’t count stars, it connects your restaurant's reputation data to your operational truth.
By analyzing sentiment patterns across languages, dialects, and context, Sira detects when polite reviews mask real issues. It links feedback trends to operational performance, from prep speed to delivery timing, revealing where experience breaks before customers walk away
