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Sentiment analysis

AI brand sentiment: how AI describes you when it names you

An assistant can name your brand and, in the same breath, call you the expensive one with the hard setup. That counts as a mention, and it loses the deal. We rate whether you are recommended, listed or caveated, keep the sentence behind each rating, and show the pages the description came from, so you know what to change.

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How you are framed

Three sentiment ratings, and the text behind each

Applied to the passage about your brand, not to the answer as a whole, and counted out of the mentions that could be rated at all. The three sentences beside this all named one rival: one recommends it, one warns about it, one only lists it.

Recommended.
You are put forward as an answer to the question, not merely listed among the options.
Neutral.
You are listed. Named without a judgement attached: given as an option, an example or a fact.
Negative.
You are named with a caveat: price, complexity, a missing feature, a comparison you lose.
Not clear enough to call.
The classifier abstains rather than guessing. Those mentions are counted, never rated.

Auditable, or it is worthless

Every rating shows its working

A sentiment score with nothing behind it is a number you have to take on faith, and nobody should. Each rating stores the passage it was drawn from, the phrases that produced it and a confidence value, so you can read the words yourself and decide. This is the same standard we hold every other number to on the methodology page. The answer beside this names you second and qualifies you in the same sentence: a mention every counter scores as a win.

The passage is kept.
Read the sentence that was rated, in the full answer it came from.
A confidence on every rating.
Stored with it. Below a set floor there is no rating at all, rather than a guess forced into a bucket.
Per mention, not per answer.
One answer can praise your category and doubt you. Those are two facts.

Upstream of the answer

Negative sentiment usually has an address

Assistants describe your category from what the web says about it. That means criticism has a source, and a source can be worked on.

When an assistant keeps describing you as hard to implement, it did not form that view on its own. It is compressing what it read: a comparison page written by a competitor, a forum thread from two years ago, a review site whose entry nobody has updated since your last redesign. The description follows the sources, so the fix is upstream of the assistant. Correct the page, or get a fairer one in front of the engine, and the scheduled re-checks show whether the description moved.

Sentiment beside sources.
The framing sits next to the citations rather than in a chart of its own. Read the rating, then what the answer cited, and a recurring criticism usually traces to two or three pages you can do something about.
Told when it turns.
When caveats start to outnumber recommendations, monitoring tells you rather than you noticing a quarter later.
No single happiness score.
We will not reduce six engines and a hundred passages to one number out of ten.

Common questions about AI sentiment analysis

What people ask before they act on a sentiment rating. If yours is not here, ask us.

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What is AI sentiment analysis?
Rating how an assistant describes your brand in the answers where it names you: recommended, listed, or named with a caveat. Getting recommended by AI means moving mentions into the first of those. Being mentioned is neutral information on its own; being mentioned as the expensive option, or the one with the difficult onboarding, is a different fact entirely and it is the one that costs you deals.
Do you rate the whole answer or just the part about my brand?
Just the part about your brand. An answer can be enthusiastic about your category and lukewarm about you specifically, and rating the whole passage would average those two into something meaningless. We rate the sentence that names you, with the one either side of it for context, and we keep it.
Can I see why something was rated negative?
Yes, and this is the part we would insist on. Every rating stores the phrases that produced it and the passage it came from, so you can read the actual words and disagree with us. A sentiment score you cannot audit is a number you should not act on.
How accurate is the rating?
Each rating carries a confidence value, and below a set floor the classifier abstains rather than guessing, so an unclear mention is counted as unclassified instead of being forced into a bucket. Language about brands is genuinely ambiguous: 'a solid budget option' is praise or damnation depending on who you sell to. So we show you the text and let you make that call rather than pretending a classifier settled it.
What do I actually do about negative sentiment?
Read the passages. Assistants describe your category from what the web says about it, so a recurring criticism usually traces back to a specific comparison page, a review thread or an old pricing page that still ranks. The fix is upstream of the assistant, which is why we show the citations beside the sentiment.
Is sentiment tracked on every engine?
Yes. Answers from all six engines are classified, on every plan. The framing column on your Overview pools them per brand with the denominator stated, and every answer behind it is kept with the engine that gave it, so you can read how each one described you.

Related reading

The rest of what we measure

Six more, all on every plan.

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Find out if AI recommends you or just lists you

Add your brand and the prompts your buyers type, and read how each answer describes you within minutes.

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On every plan

  • Every answer stored in full, with its citations.
  • Every rate shown with the number of checks behind it.
  • Cited-but-not-named reported as its own state.