What is AI brand sentiment?
AI brand sentiment is the tone in which AI assistants such as ChatGPT, Gemini and Perplexity describe your brand: positive, neutral or negative. It covers the words and associations that come with an AI brand mention, such as “reliable”, “expensive”, “outdated” or “best for beginners”.
The idea comes from classic brand sentiment analysis, where marketers classify reviews, social posts and news coverage to see how people feel about a brand. AI brand sentiment applies the same lens to a new place: the answers AI models give when someone asks for advice. Because those answers are built from reviews, forums, articles and the brand's own content, they tend to reflect the overall tone of the sources behind them.
Why AI brand sentiment matters
A mention only helps if it works in your favor. When an assistant names you but adds that customers complain about slow support, you are in the answer and still losing the sale. A brand mentioned fifty times with criticism can be worse off than a competitor mentioned ten times with praise, as the Trackbase blog explains in why sentiment decides who wins.
AI answers also concentrate opinion. A buyer who reads one summary sees a single verdict instead of browsing twenty reviews, so a negative framing carries more weight. That is why sentiment is one of the five factors in the Trackbase AI Visibility Score, next to presence, position, frequency and comparison with competitors.
How to measure and improve AI brand sentiment
Run a fixed set of prompts about your brand and category across several AI models, and repeat them regularly, because answers change.
Classify each mention as positive, neutral or negative and note the exact words used.
Trace negative or outdated claims to the sources cited next to them: an old review thread, a critical comparison article or an outdated page on your own site.
Fix what is real (the product issue, the unclear pricing), then update or add sources that reflect the current situation.
Follow the trend per model and against competitors instead of reacting to single answers.
Negative sentiment is often the first sign that you need AI reputation management. Trackbase reports sentiment across 11 AI models every day, so PR and brand teams can follow how the AI narrative about their brand develops.
Examples of AI brand sentiment
Positive: asked for the best project management tool for small agencies, an assistant calls a B2B software vendor “quick to set up and good value for small teams”.
Neutral: a Dutch bicycle webshop appears in a list of places to buy e-bikes, without any judgment.
Negative: an assistant warns that a meal-kit brand has “frequent delivery problems”, based on forum threads from two years ago.
Mixed: a hotel chain is praised for its locations but called “dated”, because older reviews dominate the cited sources.
Frequently asked questions
How do AI models decide how to describe my brand?
They draw on patterns in their training data and, when they search the web, on the sources they retrieve for the question. Reviews, forums, comparison articles, news coverage and your own website all feed into that picture. If recent sources praise your service, answers tend to follow. If old complaints dominate, they can shape the tone even after you have fixed the problem.
Can I change negative AI sentiment about my brand?
Yes, but indirectly. You cannot edit an AI answer, so you change the sources behind it. Fix the underlying issue if there is one, publish clear and current information on your own site, ask satisfied customers for reviews and respond constructively where complaints live. Then track the same prompts over several weeks, because models and their sources update at different speeds.
Is neutral sentiment a problem?
Not necessarily. Many factual answers are neutral by nature, such as a simple list of providers. It becomes a problem when competitors in the same answer get positive framing and you do not, because the assistant is then effectively recommending them over you. Compare your sentiment with competitors on the same prompts rather than judging it in isolation.