Brand sentiment without the vanity score

Most AI visibility tools classify how assistants describe your brand: positive, neutral, negative, sometimes mixed. Averaged up, that becomes a sentiment score. It is a reasonable alarm bell. It is a poor guide to what to do next.

How tools sort descriptions

Note the small differences. Some tools have a “mixed” bucket for answers that praise one thing and criticise another; others force a single label. Some count answers where tone is unclear; others leave them out. As with every metric in this category, a sentiment percentage from one tool isn’t directly comparable with another’s.

Why the score flatters

An answer that recommends a brand will, almost by definition, describe it in neutral or positive terms. So a brand that is recommended often can show healthy sentiment even when something important in those answers is wrong. The useful signal is usually in the detail, not the average.

Read for four things instead

A spreadsheet with those four columns, filled from a dozen real answers, is often more useful than a year of sentiment charts. Some tools now help directly: Waikay, for instance, lists fact checking and topic analysis, and says it uncovers hallucinations and missing knowledge.

From description to action

1. CollectTen to twenty answers that mention you, from the platforms that matter.
2. TagAccuracy errors, positioning, objections, comparisons.
3. TraceCheck the cited pages for where each claim comes from.
4. Fix the sourceUpdate your pages, correct listings, answer the objection publicly.

Step three links sentiment to citations: wrong facts usually come from somewhere, and the cited page is the first place to look.