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Glossary term

Brand sentiment

Whether AI answers describe your brand positively, neutrally or negatively when they mention it.

In depth

What it really means

Being named is not the win. An assistant that says ‘a solid mid-market option with strong support’ and one that says ‘known for billing issues and slow onboarding’ both count as a mention. Only one of them sells anything.

Sentiment is inherited from the web, not from your site. Models learn how to describe you from review sites, forums, comparison posts, Reddit threads and press. Your own marketing copy is the least trusted input in that mix, which is why fixing sentiment is a reputation job before it is a content job.

How it works

  1. The model has absorbed descriptions of your brand from training data.
  2. At answer time it may also retrieve current pages about you.
  3. It weighs sources by perceived trust. Independent review sites and established publications outweigh your homepage.
  4. It generates a characterization, usually a short qualifier attached to your name.
  5. That qualifier is what the buyer reads, and it persists across many answers.
the formula

Sentiment score = (positive mentions – negative mentions) / total mentions, across a fixed query set

Run it per assistant. ChatGPT, Claude, Gemini and Perplexity draw on different source mixes and often disagree about the same brand. Score the qualifier, not the sentence. ‘X is good for small teams but lacks enterprise controls’ on an enterprise query is a negative, even though it reads neutral.

Pros & cons

Pros

  • Sentiment is measurable with a repeatable prompt set, unlike most brand perception work.
  • Fixing the source usually fixes many answers at once, because assistants draw on the same handful of trusted pages.
  • It surfaces objections your sales team is already hearing but cannot prove.

Cons

  • You are managing something you do not control, on infrastructure you cannot see into.
  • Model updates can change your characterization overnight for reasons unrelated to anything you did.
  • Outdated criticism can persist in training data long after you fixed the underlying problem.

Common mistakes

  • Tracking mention count and ignoring the adjective attached to it.
  • Running one prompt once. Assistants vary between runs, so a single answer tells you nothing.
  • Trying to fix sentiment by publishing more of your own marketing copy, which is the source models trust least.
  • Ignoring Reddit, G2 and comparison posts, which are heavily weighted and rarely owned by you.
  • Treating a fixed product problem as fixed everywhere. The old complaints are still on the pages the model reads.

Best practices

FAQs

What is brand sentiment in AI answers?

The tone an AI assistant takes when it mentions your brand: recommending it, describing it neutrally, or warning against it. It is the adjective attached to your name, not whether your name appears.

Why does AI sentiment matter more than mention count?

Because a negative mention actively costs you deals. An assistant describing you as ‘limited for enterprise’ on an enterprise buying query does more damage than not appearing at all.

Where does AI sentiment come from?

From what the wider web says about you: reviews, forums, comparison articles and press. Models weight independent sources above your own marketing, so your site has less influence than you would expect.

How do I improve AI brand sentiment?

Find the specific sources driving the characterization and fix the information there. Then get accurate, current descriptions onto independent sites your buyers trust. Publishing more of your own copy moves this very little.

How often should I check it?

Monthly is enough for most brands. Use the same prompts every time, run each prompt several times, and track the trend rather than any single answer.

Keep reading

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