Naver sentiment tracking
Naver's blog and cafe corpora are conversational, so sentiment here reflects Korean consumer register rather than reference writing. Classification runs on the Korean text with the evidence sentence retained.
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
What the API returns
A single collection call against Naver for the sentiment field, sampled n times and returned as one object per prompt.
{
"engine": "naver",
"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 →Naver sentiment FAQ
- Which language is classification run in?
- Korean, on the original text, with the source sentence returned for audit.
- Does politeness level matter?
- Considerably. It is part of what the classifier reads.
Get Korean sentiment classified in Korean
Send a Korean prompt set and we will return labels with source-language evidence.
Across every engine
Compare how sentiment behaves on every engine we track.
Sentiment across all engines →