Sentiment Score

The obvious way to score sentiment is to label each mention positive, neutral, or negative and tally them up. In AI answers, that approach breaks down almost immediately.

Models rarely speak badly of brands. When a model recommends software, cars, or clinics, it tends to describe every option in broadly favorable, safe language. Run naive sentiment analysis and nearly everyone scores 85% positive, including your weakest competitor. The signal is drowned in uniform politeness. A raw positive read tells you the model is agreeable, not that you are winning.

The differences that matter are subtle: warmth versus faint praise, "the leading choice for X" versus "also an option," an enthusiastic description versus a bare factual one. Those distinctions are real and they compound across thousands of answers, but they are invisible on a crude positive/negative axis.

This is why a useful Sentiment Score is calibrated, not absolute. Instead of asking "is this mention positive?" it asks "how does the tone about this brand compare with the tone about the category as a whole?"

That baseline changes everything. If every brand in your category is described in glowing terms, being described glowingly is par, not an advantage. What matters is whether the models are warmer or cooler about you than about the field. A calibrated score surfaces that: it can show you sitting below the category baseline even when every individual mention of you reads as vaguely positive, because everyone else reads as more positive still.

Calibration converts a flattering-but-flat measurement into a comparative one that can actually move and actually inform decisions.

A Sentiment Score measures the tone of how you are described. It is deliberately separate from other axes so each stays interpretable:

• It does not measure how often you appear; that is Share of Answers.
• It does not measure whether the model's description matches your intended positioning; that is brand voice alignment, a question of accuracy rather than warmth.
• It does not measure factual correctness; a model can describe you warmly and still get the facts wrong.

Keeping these distinct matters. A brand can be mentioned constantly, in a warm tone, yet described in a way that misses what it actually stands for. High sentiment does not guarantee the narrative is right, only that it is friendly.

Sentiment Score is the quantified expression of Brand AI Sentiment, the broader idea that the tone models use about you is itself a measurable brand asset. The concept is "how do models feel about us"; the score is the number that tracks it over time and across engines.

Because tone is only meaningful in context, the score leans on competitive benchmarking: it is computed against the brands the models genuinely name in your category, not a curated few. Prominence AI tracks sentiment this way across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews and AI Mode, so a change in the score reflects a real shift in how favorably you are described relative to the field.

Why is my Sentiment Score not near 100 when every mention sounds positive?

Because the score is calibrated to your category, not to an absolute scale. If competitors are described even more warmly than you, you sit below the baseline even though each individual mention reads as positive. The score measures relative warmth, which is where the real signal lives.

Does a high Sentiment Score mean the models describe my brand accurately?

Not necessarily. Sentiment measures warmth of tone, not accuracy of positioning. You can be described positively yet inaccurately. To check whether the description matches how you want to be seen, look at brand voice alignment alongside the score.