AppearIn AI
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Sentiment

Sentiment measures not just whether AI mentions you, but how it talks about you, favorably, neutrally, or critically. A brand can be highly visible and still be described in a way that costs it customers.

How sentiment is scored

For every answer that mentions your brand, AppearIn AI reads the surrounding language and classifies how the brand is framed. Each mention lands in one of three buckets:

Positivefavorable framing

The brand is recommended, praised, or described with clear strengths, "a great choice for teams that need X."

Neutralfactual framing

The brand is named without a value judgement, listed as an option, described factually, or compared without a verdict.

Negativeunfavorable framing

The brand is described with caveats, criticism, or as the weaker option, "limited integrations" or "better suited to small teams."

These roll up into an overall sentiment reading for the brand, tracked over time and per engine. Different engines can frame the same brand differently, which is why we keep them separate.

Neutral is the default, not a problem

Most factual mentions are neutral, and that's fine. Watch for a shift toward negative, or a competitor pulling consistently positive framing while you stay neutral, those are the meaningful signals.

Sentiment drivers

A single sentiment number tells you something changed; it doesn't tell you why. AppearIn AI surfaces the drivers, the recurring themes the engines attach to your brand, like pricing, support, integrations, ease of use, or performance. When sentiment dips, the drivers show whether it's a pricing perception, a support reputation, or a missing capability that's pulling the average down.

Drivers are where sentiment becomes actionable. "Negative on pricing across three engines" is something a marketing or product team can respond to; a bare score isn't.

Reading sentiment well

  • Read it next to visibility.Negative framing on a brand you're highly visible for matters more than negative framing on a rarely mentioned one.
  • Compare against competitors. If rivals are described more warmly for the same use case, that gap is a positioning problem worth investigating.
  • Check the transcripts. The saved answers show the exact wording behind a classification, so you can see precisely what the engine said.