AI Sentiment
The tone (positive, negative, neutral) with which AI platforms describe your brand in their responses.
What is AI Sentiment?
AI sentiment measures how a brand is framed when an engine mentions it. The same prompt set used for mention tracking is parsed for the language around each brand mention: is the brand recommended, praised for specific strengths, described neutrally, qualified with caveats, or criticised? Each mention is classified, and the results are aggregated into a sentiment distribution per engine, per topic and over time.
Sentiment in AI answers is inherited from sources. If the review sites, community threads and articles an engine retrieves describe a product as expensive or hard to set up, the answer will say so, often in the same words. If the brand's own pages are vague about what the product does, the engine fills the gap with whatever third parties say. Sentiment analysis therefore doubles as source analysis: a negative pattern usually traces back to a small number of pages the engines keep citing.
The metric also catches accuracy problems that a simple positive or negative label misses. An answer that praises a feature the product does not have, or recommends it for a use case it does not serve, reads as positive but sets buyers up for disappointment. Good sentiment tracking flags these as inaccuracies alongside tone.
Why it matters for AI search
Being mentioned is not enough if the mention comes with a caveat that steers the buyer elsewhere. AI sentiment shows how the brand is actually characterised in answers, which engines carry negative framing, and which sources are feeding it. That turns a vague brand-reputation concern into a concrete list: pages to correct, reviews to respond to, communities to engage, and owned content to publish so that the engines have a clearer, more accurate account to draw from.
Related terms
Brand Mention
An instance where an AI platform explicitly names your brand in a response, whether as a recommendation, comparison, or reference.
AI Visibility
The measure of how often and prominently a brand is mentioned, cited, or recommended in AI-generated responses across platforms like ChatGPT, Claude, and Perplexity.
Hallucination
When an AI model generates factually incorrect information confidently. Monitoring hallucinations is critical for brand safety.
Source Attestation
The process by which an AI model verifies the authority and truthfulness of a source before citing it in an answer.
Frequently asked questions
How is AI sentiment classified?+
Each brand mention in a tracked answer is classified as positive, neutral or negative, usually by a language model applying a consistent rubric, sometimes with human review on a sample. The classification looks at the sentences around the mention, not the whole answer, so a brand can be positive in an answer that is negative about a competitor.
Can a brand change how AI engines describe it?+
Yes, over time. Engines repeat what their sources say, so correcting inaccuracies on owned pages, addressing recurring complaints on review platforms, and earning coverage that describes the product accurately all shift the material the engines retrieve. Re-running the prompt set shows when the change has landed.