ChatGPT sentiment tracking
ChatGPT is the surface most likely to make an explicit recommendation, which makes sentiment more decisive here than anywhere else we track.
How it renders
Derived from the answer text with the classifying sentence retained.
Sampling
Classifications are reported as distributions across n, because the same engine will frame the same brand differently between samples.
Gotcha
What the API returns
A single collection call against ChatGPT for the sentiment field, sampled n times and returned as one object per prompt.
{
"engine": "chatgpt",
"field": "sentiment",
"prompt": "best ai visibility tracking tool",
"country": "GB",
"language": "en",
"n": 30,
"sentiment": [
{
"brand": "Nostimates",
"label": "recommended",
"confidence": 0.86,
"distribution": {
"recommended": 0.6,
"neutral": 0.33,
"hedged": 0.07
}
}
]
}Response fields
| Key | Type | Notes |
|---|---|---|
| brand | string | Brand the classification applies to. |
| label | string | recommended | neutral | hedged | negative. |
| confidence | float | Classifier confidence for this sample. |
| evidence | string | The sentence the label was drawn from. |
| distribution | object | Label shares across the n samples. |
What it costs
Add-on field. Priced per collection at a small multiple of the base credit.
See credit pricing →ChatGPT sentiment FAQ
- Why track sentiment on ChatGPT specifically?
- Because it gives direct recommendations. Knowing whether you are the pick or the cautionary example changes what the client should do next.
- How do you audit labels?
- Every label ships with its evidence sentence in the export.
Find out whether ChatGPT recommends your client
Send a prompt set and we'll return the sentiment distribution with evidence.
Other ChatGPT data
Across every engine
Compare how sentiment behaves on every engine we track.
Sentiment across all engines →