Sentiment Analysis (AI Mentions)

Counting how often an AI engine names your brand only tells half the story. A mention that frames you as expensive, dated or a distant runner-up can do more damage than no mention at all, because users treat AI answers as neutral expert advice. Measuring sentiment alongside mention rate separates visibility wins from reputation problems: two brands with identical AI visibility can be positioned completely differently inside the same answer.

Sentiment is typically scored on a scale from negative to positive for each individual mention, then aggregated per prompt, per topic and per AI provider. The aggregation matters because a single glowing answer can mask a pattern of lukewarm framing across the prompts your actual buyers ask.

The standard approach extracts every brand mention from collected AI answers, captures the surrounding context, and classifies the framing as positive, neutral or negative, often on a continuous score from -1 to 1. Context is essential: the sentence "cheaper than X but less reliable" is positive on price and negative on quality, so good systems look at the full passage rather than keywords.

Because of answer volatility, sentiment must be sampled repeatedly over time rather than spot-checked once. A brand can read as positive in one run and neutral the next, so trends across daily samples are more trustworthy than any single answer. Tracking by provider also matters, since models trained on different data often frame the same brand differently.

Negative or lukewarm framing usually traces back to sources the model leans on: outdated reviews, old pricing pages, or competitor comparison content. Fixing sentiment means finding and influencing those sources, refreshing your own pages, and earning third-party coverage that reframes the narrative, a core part of any GEO strategy. Platforms like Geonimo score the sentiment of every brand mention per provider through built-in sentiment analysis, so teams can see exactly which engines and prompts drive negative framing and prioritize fixes accordingly.