Feature
Sentiment Analysis
Not just how often AI mentions you — how it talks about you.
Being mentioned by AI engines is only half the story. "X is the market leader" and "X is a pricier option with mixed reviews" are both mentions — with opposite effects on buyers. Geonimo scores the sentiment of every mention, positive to negative.
Sentiment is tracked per mention, per provider and over time, so you can see whether AI's tone about your brand is improving, where it's most negative and how it compares across engines.
Per-mention scoring
Every extracted mention gets a sentiment score on a positive-to-negative scale, based on the context the AI engine actually wrote — not the mere fact of being named.
Per-provider breakdown
Engines can disagree about you: warm on Claude, lukewarm on Gemini. The provider breakdown shows where your brand narrative is strongest and which engine needs attention.
Sentiment trends
Sentiment is charted over time, so you can watch the tone shift after a product launch, a PR push or a wave of negative reviews entering the engines' source material.
Context preserved
Each scored mention keeps its surrounding context, so you can read exactly what the AI said — the specific praise or criticism — instead of trusting a number blind.
AI engines editorialize — constantly
AI answers don't list brands neutrally; they characterize them. One brand is "the popular choice", another "powerful but complex", a third gets a caveat about pricing in every answer. These framings function as AI recommendations and shape buyer perception at scale, because thousands of people receive nearly the same characterization. A brand with strong mention volume but negative framing can be losing deals from the very answers that look like wins in a mentions-only dashboard.
Scoring the tone behind every mention
When Geonimo extracts a mention from an AI answer, it analyzes the surrounding context and assigns a sentiment score from negative to positive. Scores aggregate into provider-level views and time-series trends, while individual mentions keep their context for reading. This gives you AI sentiment analysis at three altitudes: the overall narrative, the per-engine differences, and the specific sentences shaping how engines — and through them, buyers — perceive your brand.
Managing your AI narrative
Sentiment data tells you which battles to fight. Recurring negative framing usually traces to sources the engines trust — old reviews, an outdated comparison, a critical thread — and fixing the source fixes the narrative over time. Provider gaps tell you where to focus content and PR. And sentiment trends are the honest scoreboard for brand perception work: if your repositioning is landing, the tone of AI answers will show it within weeks, long before brand surveys do.
Frequently asked questions
How does Geonimo determine the sentiment of a mention?
Each mention is scored based on the context the AI engine wrote around your brand — praise, criticism, caveats, comparisons. Scores run on a negative-to-positive scale and the original context is preserved, so you can always read the underlying sentence behind any score.
Can sentiment differ between AI engines?
Yes, and it frequently does — engines draw on different sources and weigh them differently, so one can frame you positively while another repeats criticism. The per-provider breakdown exposes these differences so you can target fixes at the engine that needs them.
What can I actually do about negative AI sentiment?
Trace it to its sources. Negative framing usually echoes specific cited content — dated reviews, unfavorable comparisons. Using sentiment alongside citation tracking, you find those sources, then address them with updated content, outreach or PR. Sentiment trends then verify the fix worked.
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