Sentiment

Score how each AI model talks about a brand, positive / neutral / negative, with the phrases driving the tone.

Overview

Not just whether a model mentions a brand, but how it talks about it. Per model you get a positive/neutral/negative split (summing to ~1) and the verbatim phrases driving it, plus an aggregate across all models queried.

Reference

POST/v1/sentiment

$0.09/model · billed per model queried, so all 5 models = $0.45. Pass models to query fewer.

Parameters

`query`required	string	The query whose answers are scored for brand sentiment.
`brand`required	string	The brand to score sentiment for in each model answer.
`models`optional	array of enum (chatgpt \| claude \| perplexity \| gemini \| google_ai_overview)	Models to query. Defaults to all.

Request

curl https://api.cite42.dev/v1/sentiment \
  -X POST \
  -H "Authorization: Bearer $CITE42_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "query": "is attio a good CRM?", "brand": "attio" }'

Response

{
  "query": "is attio a good CRM?",
  "brand": "attio",
  "perModel": [
    {
      "model": "chatgpt",
      "positive": 0.55, "neutral": 0.35, "negative": 0.10,
      "phrases": ["modern, AI-native CRM", "polished UI"]
    }
  ],
  "aggregate": { "positive": 0.62, "neutral": 0.30, "negative": 0.08, "phrases": ["…"] },
  "requestId": "…",
  "billing": { "cost": { "usd": "0.36" }, "balance": { "usd": "13.56" } }
}