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GEO Platforms With LLM Brand Sentiment Analysis (2026) | GEOly | GEO-Datenplattform für DTC-Marken
Which GEO Platforms Have a Brand Sentiment Analysis Layer?
Zusammenfassung
Sentiment scoring — how positively or negatively each LLM describes your brand across all tracked prompts — is now a mainstream GEO feature. 16+ platforms ship it, from Evertune and Conductor to GEOly's built-in Sentiment score. Here's who has it and what to look for.
2026/07/15
10 Min. Lesezeit
Ranking used to be a binary game: you were on page one or you weren't. AI answers work differently. When someone asks ChatGPT, Gemini or Perplexity about your category, the model doesn't just list you — it describes you, in a full sentence, with a tone. "A reliable budget pick." "Popular but their support is hit or miss." "The premium option most reviewers recommend." That tone travels straight into a buying decision, and you never see it unless something is watching for it.
So the question is a good one: which GEO platforms actually include a sentiment analysis layer that scores how positively or negatively each LLM talks about your brand — not on one prompt, but aggregated across every prompt you track? The short answer: most of the serious ones now do. Sentiment has gone from a nice-to-have to a standard column in the AI visibility category. The longer answer is that the implementations vary a lot, and a few well-known tools still don't have it at all.
Key Takeaways
A brand sentiment layer parses every AI answer that mentions you, classifies the tone (positive, neutral, mixed, negative), and rolls it up across prompts, models and time — it is not social-media sentiment.
The capability is now mainstream: 16+ GEO/AEO platforms explicitly market a sentiment feature, including Profound, Peec AI, Otterly, Scrunch, Rankscale, Semrush, Nightwatch, Knowatoa, Conductor, BrightEdge and Evertune.
The deepest implementations are Evertune (a −100 to +100 score from weighted word associations), Conductor (sentiment broken out by business driver like price and service), Rankscale (17+ engines with a keyword-driven radar) and Otterly (a quantified Net Sentiment Score).
Not every tool has it: Ahrefs Brand Radar leaves sentiment as a manual read of the responses, and Waikay rejects polarity scoring on purpose in favor of factual accuracy.
Tone concentrates where it hurts: BrightEdge found ChatGPT surfaced negative brand sentiment 19.4% of the time during the consideration-to-purchase phase, versus 1.5% for Google AI Overviews.
What a "sentiment analysis layer" actually means
It is easy to confuse this with the social-listening sentiment you already know. It is not the same thing. A GEO sentiment layer isn't reading tweets — it is reading the model's own words about you inside its answers.
Mechanically, four things have to happen. The platform runs your tracked prompts across the AI engines on a schedule. It captures each answer that names your brand. It classifies the tone of that specific mention. Then it aggregates: a share of positive versus negative, a single score, a trend line, and — if the tool is any good — a way to drill from the number down to the exact prompt and model that produced a bad description.
That last part matters more than the headline number. "Your sentiment is 72" is a vanity metric. "Perplexity calls your onboarding confusing on three pricing-comparison prompts" is something you can fix. When you evaluate tools, judge them on whether they can take you from the aggregate to the offending answer.
GEOly monitoring dashboard tracking brand visibility and citations across prompts and AI models — Source: app.geoly.ai
Because the score is only as meaningful as the prompt set behind it, sentiment and prompt research are two halves of the same job — a favorable score across ten prompts nobody asks tells you nothing.
Why the tone of AI answers is now a revenue problem
The reason vendors rushed to ship this is that negative AI descriptions don't sit harmlessly in a dashboard — they land at the worst possible moment.
BrightEdge's AI Catalyst research put numbers on it. Across mentions overall, Google AI Overviews surfaced negative brand sentiment about 44% more often than ChatGPT. But the sharper finding was about timing: during the consideration-to-purchase phase specifically, ChatGPT surfaced negative sentiment 19.4% of the time versus Google's 1.5% — roughly thirteen times higher — concentrating criticism exactly where the buying decision happens. You can read the underlying data in BrightEdge's release on AI Overviews and brand sentiment.
And these descriptions reach people whether they ask for them or not. In Gartner's consumer research, 72% of consumers said generative AI shows up in their internet and app use "whether I asked for it or not," and 54% said they had to double-check the accuracy of what those tools told them. If the default, unsolicited description of your brand skews negative, you are losing consideration before a human ever forms an opinion. That is why a sentiment layer belongs next to your brand reputation monitoring, not in a separate silo.
The GEO platforms that include a sentiment layer
Here is the current landscape, grouped by how they approach it. Every platform below explicitly markets a sentiment feature; the links go to the vendor's own page so you can verify the depth before you commit.
Purpose-built sentiment depth — where scoring is the differentiator:
Evertune — a Brand Sentiment Score from −100 to +100 built on per-word associations, weighted by frequency and tracked across roughly eleven models over time, with statistical significance behind the number.
Conductor — positive/negative/neutral broken out by business driver (price, service, quality), per engine, over time, and tied back to the source URLs and prompts that drove it.
Rankscale — a Brand Perception Engine that scores tone across 17+ engines and extracts the keywords driving it, plus a five-dimension Sentiment Radar for competitor comparison.
Otterly.AI — a Net Sentiment Score from −100 to +100 alongside positive/neutral/negative percentages, at both brand and per-prompt level, benchmarked against competitors.
Full-suite GEO platforms with sentiment built in:
Profound — a Sentiment Score broken down by prompt, topic, tag and platform, with narrative themes and trends over time.
Peec AI — sentiment as a core metric, per model and over time, filterable by prompt, theme and country.
Scrunch AI — Sentiment Trends (positive/mixed/negative) tracked weekly, by model, topic and buyer persona.
Semrush AI Toolkit — a Sentiment Analysis report showing which positive and negative topics LLMs associate with your brand, over time and against competitors.
Nightwatch — citation-level sentiment (for example, "85% favorable") across five engines, with flags when the model misrepresents you.
Knowatoa — a dedicated Sentiment Monitoring feature scoring every response daily across seven platforms, with competitor comparison.
Brandlight — positive/negative/neutral trends over time alongside share of voice, across roughly eleven engines.
Goodie — sentiment segmented by model, geography, persona, language and topic, refreshed daily.
AthenaHQ — sentiment inside its PR-monitoring module, with the adjectives and topics that position your brand across ChatGPT, Claude and Perplexity.
Trakkr — a Perception view that scores the AI's words across 20 dimensions with positive and negative quotes, per model and timestamped.
BrightEdge — presence and sentiment inside AI Catalyst, including which attributes each engine favors.
Partial, or notably absent — worth knowing so you don't over-buy:
SE Ranking exposes sentiment tracking only through its SE Visible add-on, and Superlines lists sentiment as a dashboard metric without a documented positive/neutral/negative method — treat both as partial.
Ahrefs Brand Radar tracks mentions, share of voice and citations, but sentiment is a manual read of the responses tab — there is no automated scoring layer.
Waikay deliberately skips polarity scoring, arguing that factual accuracy matters more than a positive/negative label.
How GEOly scores brand sentiment across your prompts
GEOly builds sentiment directly into its Analysis workspace, so it sits next to visibility, citations and share of voice rather than in a bolt-on. Point it at your tracked prompts, and for every AI answer that mentions your brand it classifies the tone and rolls it into one view.
GEOly Sentiment analysis view scoring AI brand mentions as positive, neutral, mixed or negative with a 0-100 sentiment score and trend line — Source: app.geoly.ai
What you get is a single Sentiment Score on a 0–100 scale (positive, neutral or negative at a glance), a distribution that splits mentions into positive, neutral, mixed and negative counts, an explicit positive rate and negative rate, and a Sentiment Trends chart that shows how the tone moves day by day. You can filter it by platform, so you can see whether ChatGPT is warmer about you than Gemini or Perplexity, and pair it with competitive analysis to check whether a rival is quietly winning the tone war. When it's time to report up, the same numbers drop into a GEO report without re-keying anything. If you already track AI visibility with GEOly, sentiment is the layer that tells you not just whether you show up, but whether the model likes you when you do.
How to choose a sentiment layer
Most vendors will show you a green number. Pressure-test it against six questions before you trust it:
Does it break sentiment down per model, or blend every engine into one average? Per-model is the point — ChatGPT and Gemini often disagree about you.
Does it separate mixed and neutral from negative, or collapse everything into a crude positive/negative split? Granularity is where the actionable signal lives.
Can you see the trend over time, not just a snapshot? A score without a trend line can't tell you if a launch or a bad review moved the needle.
Can you drill from the aggregate score down to the exact prompt and answer that produced it? If you can't reach the offending sentence, you can't fix it.
Does it benchmark you against competitors on the same prompts? Your 70 means little without knowing the rival scores 85.
Does it tie sentiment back to the source URLs the model cited? That's how you find the review or forum thread poisoning the answer.
Score each tool on those six, and the long list above narrows fast.
FAQ
Is AI brand sentiment the same as social media sentiment?
No. Social sentiment reads what people post about you. AI brand sentiment reads what the model itself says about you inside its answers — the description a shopper actually sees when they ask ChatGPT or Perplexity for a recommendation. Different source, different fix.
Can I track sentiment separately for each LLM?
Yes, and you should. The better tools — including GEOly, Rankscale, Peec AI and Evertune — break sentiment down by model, because a brand can read as "the trusted pick" on one engine and "overpriced" on another. A blended average hides exactly the gap you need to close.
Which GEO tools do not have a sentiment layer?
Ahrefs Brand Radar tracks mentions and share of voice but leaves sentiment as a manual read with no automated scoring. Waikay skips it by design, focusing on factual accuracy instead of tone. SE Ranking and Superlines offer only partial or thinly documented versions.
How often does AI sentiment update?
It depends on the tool and your plan, ranging from daily refreshes (Knowatoa, Goodie, GEOly) to weekly rollups (Scrunch). For a fast-moving launch or a reputation issue, favor a platform that re-scores daily so you catch a swing before it compounds.
Does GEOly include brand sentiment analysis?
Yes. It's built into GEOly's Analysis workspace as a dedicated Sentiment view — a 0–100 score, a positive/neutral/mixed/negative distribution, positive and negative rates, and a trend line, all filterable by AI platform and drawn from the prompts you already track. You can see how the GEOly editorial team covers AI visibility for more on the methodology.
The takeaway
A sentiment layer is no longer the thing that separates premium GEO tools from the rest — it is table stakes, and the real decision is about depth. If all you need is a directional read, almost any full-suite platform will do. If tone is a genuine revenue lever for you — because your category is competitive and the AI's default description is doing your first round of selling — reach for the tools that score per model, separate mixed from negative, trend over time, and let you drill to the offending prompt. Start by watching how the models describe you today; you can't fix a tone you can't see.
