AI brand sentiment

The AI brand-sentiment MCP + API: not just if AI mentions you, but how. Positive, neutral, or negative, per model.

A 70% mention rate is meaningless if half those mentions say "X is overpriced and slow." /v1/sentiment scores how each model talks about your brand, positive, neutral, negative, and pulls the exact phrases driving the tone, so you can see which model is souring and why.

$1 free on signup
Credits never expire
No subscription
MCP native

Get $1 free, try it Install MCP

POST /v1/sentimentlive

1. You ask your AI, in plain English
How does AI talk about penflow, good or bad?

2. Cite42 MCP scores every model for you
→ ChatGPT · Claude · Perplexity · Gemini

3. You get back, in plain English
62% positive overall — but Claude skews negative.

WHAT YOU CAN ASK

Ask in plain language. Get structured JSON.

Each is one call against the endpoint, ask in plain English through the MCP, or hit the API directly. Same key, same credit balance.

You ask“How does AI talk about my brand?”

You get{ "positive": 0.62, "neutral": 0.30, "negative": 0.08 }

You ask“Which model is most negative about us?”

You get{ "by_model": { "claude": 0.51, "chatgpt": 0.74 } }

You ask“What phrases are driving the tone?”

You get{ "phrases": ["polished agent UI", "steep pricing"] }

Why score the tone, not just the mention

AI brand sentiment analysis: a 70% mention rate can still be 70% complaints.

Most AI-visibility tools stop at presence: does ChatGPT name you, yes or no. But "X is the polished option" and "X is overpriced and slow" both count as a mention, and they are not the same news. /v1/sentiment reads how each model actually talks about your brand and returns a positive / neutral / negative split, plus the exact phrases driving the tone.

One call fans out to ChatGPT, Claude, Perplexity, and Gemini and scores them separately, because they rarely agree. Claude tends to run critical, ChatGPT skews positive on category prompts, and Perplexity mirrors whatever sources rank that day. An aggregate score hides exactly the model that is souring on you.

See the positive/neutral/negative ratio per model, not one blended number.
Pull the literal phrases ("steep pricing", "polished agent UI") behind each score.
Re-run on a cadence and diff the negative ratio to catch a narrative before it spreads.

AI MODELS (4)

Every model, one API key.

One call reaches all 4 AI models, ChatGPT, Claude, Perplexity, and Gemini, or pass “models” to narrow to the ones you need. Billed per model queried.

ChatGPT
OpenAI
Web search on
The highest-traffic AI surface
Claude
Anthropic
Web search tool
Cited, source-grounded answers
Perplexity
Perplexity Sonar
Web-grounded
Always answers from live sources
Gemini
Google
Google Search grounding
Google's own model layer
Google AI Overviews
Google Search
Live SERP
The AI summary atop Google results

PRICED FOR REAL WORK

Real scenarios. Real numbers.

Real workflows, real call counts, real totals, see what this looks like before you commit. Credits never expire.

brand lead
Baseline how AI talks about you
Score sentiment on 20 brand-intent prompts across all four models to see the positive/neutral/negative split and the phrases behind it. 20 calls · $7.20 total.
PR / comms manager
Catch a negative narrative early
Re-run a 25-prompt sentiment sweep every week and diff the negative ratio so a souring story surfaces before it spreads. 25 calls · $9.00 per weekly sweep.
reputation consultant
Show a client their sentiment lift
Baseline 30 prompts, run the cleanup, then re-score the same 30 a month later and hand over the before/after delta. 60 calls · $21.60 across the engagement.

vs. a review-monitoring tool

Where review tools watch the source, sentiment watches what AI repeats.

Yotpo and Reputation.com score the reviews and social posts your customers leave. By the time a buyer asks an AI "is X any good?", that signal has already been paraphrased into the answer. /v1/sentiment measures that downstream layer, the one that now sits right before the purchase decision, across all four models in one call.

	Cite42	A review-monitoring tool
What it measures	How ChatGPT, Claude, Perplexity, and Gemini describe you	Raw reviews and social posts at the source
Coverage	All four AI models in one call, scored separately	Review sites and social, but not the AI answer layer
Granularity	Per-model positive/neutral/negative + the exact phrases	One blended brand score, source-by-source
Integration	One endpoint or MCP tool your agent calls directly	A dashboard you log into; export and reconcile yourself
Billing	Pay per model per call, no subscription, $25 minimum	Monthly seat-based contract
Cost, 4-model sweep	$0.36 ($0.09 per model), or $0 on a cache hit	Fixed monthly fee regardless of usage

PRICING

Pay per call. $25 minimum deposit. Credits never expire.

This page's endpoints below. See /pricing for the full per-endpoint table.

Endpoint	Price	What you get
POST/v1/sentiment	$0.09/model	Positive / neutral / negative per model with phrases, $0.09 per model per call, all four for $0.36
POST/v1/search	$0.08/model	Full prose answers per model, the raw text sentiment is scored from

See all endpoints and the model coverage table →

KEEP EXPLORING

Related endpoints

Same API key, same credit balance, different question.

AI search visibilityAI brand rankingsMention rate, position, and share of voice.Explore ai brand rankings AI citation trackerAI citationsCitations are the new backlinks.Explore ai citations AI competitor trackingAI competitor compareYour brand vs. competitors, side by side.Explore ai competitor compare

QUESTIONS

Questions about ai sentiment.

A sentiment classifier reads each model's answer about your brand and returns per-model positive/neutral/negative ratios plus the phrases driving them, so you can see if one model is more negative than the others (often it is, different models pull from different review sources). The response also includes an aggregate across all four models.

Each model has its own training data and citation sources. Claude tends to be more critical/balanced; ChatGPT skews more positive on category prompts; Perplexity reflects whatever sources rank that day. The per-model split surfaces this.

Yes. /v1/sentiment returns the exact phrases driving each model's tone, and /v1/citations finds the URLs those phrases reference, usually a critical blog post, a Reddit thread, or a competitor comparison page.

Limited. Sentiment requires meaningful mention volume. For pre-launch brands, use /v1/rankings to track when mentions start appearing, then layer sentiment monitoring on once you're showing up regularly.

Reputation tools (Yotpo, Reputation.com) score customer reviews and social posts. Cite42 surfaces how AI models *paraphrase* those signals when a buyer asks. By the time it shows up in an AI answer, it's reached the AI buyer, a few months behind raw review monitoring, much closer to actual purchase decisions.

READY WHEN YOU ARE

See how AI talks about your brand.

$1 free on signup, run a sentiment sweep on your top prompts.