AI answer sentiment describes the tone and substance of what AI assistants say about a brand. Being mentioned is only half the picture: an assistant can name your product while calling it expensive, dated or hard to set up. Tracking sentiment means recording the attributes assistants associate with you, judging whether they are fair and current, and tracing the claims back to the sources that likely produced them.
What to capture
Sentiment scores alone are crude. The useful output is the set of specific claims an assistant repeats.
- Overall tone: positive, neutral, mixed or negative
- Attributes: 'easy to use', 'pricey', 'limited integrations', 'good for enterprises'
- Recommendations: for whom the assistant suggests you, and for whom it does not
- Accuracy: whether each claim is true today
- Likely sources: cited pages, reviews and forum threads
Fixing negative or outdated sentiment
Assistants reflect what the web says about you, weighted in ways that are not published. Criticism in reviews and community discussions often shows up in answers. If a criticism is outdated, publish clear evidence of the change on your own site and make sure third-party pages can be updated. If the criticism is valid, the fix is the product, then the evidence.
Sentiment changes slowly, especially for impressions built into a model's training data. Expect retrieval-based answers to change sooner than answers drawn from model memory.
Example
An old complaint that will not go away
Fernhollow, a fictional HR platform, finds that assistants call its onboarding 'slow and complex'. Tracing citations leads to forum threads from before a major redesign. Fernhollow publishes a dated changelog entry and a short setup guide showing the new flow, updates its review profiles, and encourages recent customers to share their experiences. It then tracks whether the attribute fades over the following months.
Common mistakes
Relying on a single sentiment score
A number hides the specific claims that matter. Record the actual phrases.
Trying to suppress criticism
Fake reviews or astroturfed posts are against platform rules and can backfire. Answer criticism with real changes and evidence.
Questions
Review sites are frequently cited in AI answers about software, though weighting is not published and varies by product.
Yes, by classifying answers with a model, but automated scores need spot checks. Sarcasm, comparisons and mixed answers are often misread.
