Doubao sentiment tracking
doubaosentimentAccount required
Doubao's register is closer to social content than to reference writing, so sentiment here is more polarised than on any other engine we track.
How it renders
Derived from the source-language answer text with evidence retained.
Sampling
Classifications are reported as distributions across n, because the same engine will frame the same brand differently between samples.
Gotcha
The social register produces strong language that an enterprise-tuned classifier over-reads. We calibrate against short-video-style Chinese rather than formal prose.
What the API returns
A single collection call against Doubao for the sentiment field, sampled n times and returned as one object per prompt.
{
"engine": "doubao",
"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 →Doubao sentiment FAQ
- Why is Doubao sentiment so polarised?
- It reflects the tone of its ByteDance source corpora, which is conversational rather than encyclopaedic.
- How do you calibrate?
- Against social-register Chinese, with evidence sentences returned for audit.
Read Doubao sentiment in the right register
Send a prompt set and we will return calibrated labels with evidence.
Other Doubao data
Across every engine
Compare how sentiment behaves on every engine we track.
Sentiment across all engines →