AI Sentiment Amplification: Why 'Very Positive' Gets 8x More Visibility

"Very positive" companies average 44.7 AI visibility. "Positive" averages 5.4. That is 8x. The jump from "positive" to "very positive" is the biggest gap in our 2,014-company dataset. AI models don't just reflect sentiment. They amplify it.

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The Sentiment Distribution

Across 2,014 completed brand reports (as of Feb 15, 2026), here is how sentiment correlates with AI visibility:

Sentiment	Companies	%	Avg AI Visibility
Very Positive	248	12.3%	44.7
Positive	1,368	67.9%	5.4
Neutral	287	14.3%	2.5
Negative	100	5.0%	3.3
Very Negative	11	0.5%	4.2

The majority of companies (67.9%) fall in the "positive" bucket. Being positive is table stakes. It does not differentiate you.

The 248 companies classified as "very positive" are in a different category entirely. Their 44.7 average visibility is 8.3x the "positive" group. That is not a minor edge. It is the difference between being invisible and being recommended.

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The Paradox: Negative Beats Neutral

Look at the bottom of the table. "Negative" companies (3.3 avg visibility) score higher than "neutral" companies (2.5). And "very negative" (4.2) scores higher than both.

This seems counterintuitive. Why would negative sentiment correlate with more visibility?

The answer: controversy generates mentions. Companies with negative sentiment get discussed, debated, and argued about. AI models pick up on that volume of discussion, even if the sentiment is negative. A company nobody talks about (neutral) is less visible than a company people complain about (negative).

This does not mean you should seek negative sentiment. The 44.7 average for "very positive" dwarfs the 4.2 for "very negative." But it does mean that obscurity (neutral) is worse than controversy (negative) for visibility.

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AI Models Amplify, They Don't Create

The 8x gap between "positive" and "very positive" suggests AI models amplify existing sentiment rather than forming independent opinions.

A "very positive" company typically has: G2 leadership badges, industry awards, strong NPS scores, consistently positive Reddit and review site mentions, and favorable press coverage. AI training data absorbs all of these signals. When a user asks "best CRM for startups," the model draws on this accumulated positive signal.

A "positive" company has decent reviews and no major complaints. But it lacks the superlative signals (awards, leadership badges, enthusiastic community advocates) that push sentiment from good to exceptional.

The implication: incremental improvements in customer satisfaction may not move the needle. You need to go from "good" to "exceptional" in visible, public ways that show up in training data.

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What Drives "Very Positive" Sentiment

Based on the 248 "very positive" companies in the dataset, common patterns include:

1. G2, Capterra, or TrustRadius "Leader" badges. Review platform awards are strong training data signals.
2. Active Reddit communities. Not just mentions, but communities of advocates who recommend the product unprompted.
3. Award recognition. Industry awards, "best of" lists, and editorial picks.
4. Consistent press coverage. Not one-time PR, but repeated positive coverage over time.
5. Customer case studies and testimonials. Published on third-party sites, not just your own.

The through-line: third-party validation. AI models weight external praise more heavily than self-promotion. Five independent sources calling you "excellent" matters more than your own marketing page.

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Honest Limitation: Reverse Causation

I need to flag a big caveat. This is almost certainly a feedback loop.

Companies with high AI visibility get more traffic. More traffic leads to more customers. More customers leave more positive reviews. More positive reviews improve sentiment. Better sentiment improves AI recommendations. And the cycle continues.

Separating "does visibility cause positive sentiment" from "does positive sentiment cause visibility" is not possible with this data. Both are probably true, creating a reinforcing loop.

For the 1,368 companies stuck in the "positive" bucket, the practical question is: how do you break into the "very positive" cycle? The data suggests it starts with extraordinary customer experience that generates public, third-party praise.

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Methodology

Data source: 2,014 Loamly brand reports (completed as of Feb 15, 2026).

Sentiment classification: Derived from AI model responses about the company. Each report runs queries asking AI platforms to evaluate the company, and the overall sentiment is classified from the aggregate response patterns.

Limitation: Sentiment is classified at report generation time. It reflects how AI models perceive the company at that moment, not a historical average. Companies that recently had a PR crisis might show "negative" even if historical sentiment was positive.

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Next Steps

1. Run a free report: loamly.ai/check. See your sentiment classification and AI visibility score.
2. Understand the authority stack: Brand Authority Is 26x More Important
3. See the full benchmark: 2,014 Companies, 85.7% Invisible

No marketing spin. Just real data about your AI visibility.