Analyze how AI answers feel about your brand
Measure positive, neutral, mixed, and negative brand framing across AI-generated answers, then connect sentiment movement to prompts, competitors, citations, and reputation-risk actions.
Built for teams that need prompt-level evidence behind AI brand sentiment movement.
AI sentiment analysis
Sentiment movement
Show: Me + Top 5Brand Sentiment
Evidence Samples
Prompt-level sentiment evidence
Risk prompts firstRisk answer watchlist
Sentiment action loop
Updated weeklyProduct evidence
Sentiment analysis built for AI answer monitoring
Use sentiment as an operational signal, not a vanity score. Every movement should connect back to answer text and a next action.
Prompt sentiment map
See sentiment by category, comparison, risk, implementation, pricing, and competitor prompt groups.
Risk prompt watchlist
Monitor prompts where AI answers mention concerns, limitations, compliance questions, or competitor claims.
Source-linked evidence
Connect negative or mixed framing to cited sources, outdated pages, missing proof, and competitor narratives.
Sentiment workflow
Move from vague brand perception to evidence-backed answer sentiment
Visoryn shows which prompts, engines, competitors, and cited sources are driving sentiment changes so teams can decide what to correct, reinforce, or monitor.
Collect brand mention samples
Extract the answer snippets where AI engines describe your brand, competitors, proof points, limitations, and risks.
Classify sentiment by prompt
Separate positive, neutral, mixed, and negative framing by intent, market, engine, and buyer question.
Explain what changed
Tie sentiment movement to competitor framing, stale sources, weak proof, risky prompts, or changes in cited URLs.
Route corrective actions
Prioritize product proof, comparison copy, FAQ updates, citation fixes, and reputation-risk monitoring work.
Use cases
Where AI sentiment analysis helps
Brand and comms
Spot reputation-risk language before it becomes the default AI answer for comparison or trust prompts.
Product marketing
Identify where AI answers understate proof, misframe differentiation, or repeat competitor claims.
SEO and content
Prioritize pages, FAQs, comparison sections, and cited sources that can improve answer framing.
FAQ
Common questions
What is AI brand sentiment analysis?
AI brand sentiment analysis measures the tone and framing AI answer engines use when they mention a brand, including positive, neutral, mixed, and negative language across prompt groups.
How is this different from social sentiment analysis?
Social sentiment analyzes user posts and comments. AI brand sentiment analyzes generated answers and the cited sources, competitors, and prompt contexts that shape those answers.
Can sentiment be tied to specific prompts?
Yes. Sentiment is most useful when it is tied to the exact prompt, answer snippet, competitor context, source URL, engine, and country behind the score.
What should teams do when sentiment drops?
Review the answer text and cited sources first, then update proof, comparison copy, FAQs, support docs, third-party sources, or recommendations tied to the affected prompt group.
Next step
See how Visoryn reports your AI search surface
Start with a brand report, choose tracked prompts, and review mentions, competitors, citations, and recommendations in one workspace.