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24K Labs Sentiment Analyzer
x402
24K Labs Sentiment Analyzer is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.006/call, status unknown (last checked 2026-09-13).
Classifies text as positive, negative, or neutral and returns a numeric score, magnitude, and brief explanation.
Price / call$0.006
Success—
StatusUnknown
How this service works
Classify text sentiment (positive/negative/neutral) with a score, magnitude, and brief reason.
Output
Returns a sentiment classification label (positive, negative, or neutral), a numeric sentiment score, a magnitude value indicating intensity, and a brief human-readable reason explaining the classification.
Example use cases
Customer review tone classification
“Classify the sentiment of this product review and give me a score and reason: 'Battery life is excellent but the screen scratches way too easily.'”
Support ticket triage by mood
“Tell me whether this support ticket is positive, negative, or neutral and how strong the sentiment is: 'I've been waiting three weeks for a refund and nobody is responding to my emails. This is unacceptable.'”
Social media brand monitoring
“What's the sentiment of this tweet about our brand — positive, negative, or neutral — and what score does it get? 'Just switched to @AcmeCo and honestly it's been a breath of fresh air after years of frustration with our old vendor.'”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/sentiment-analyzeWhen to prefer
Choose this endpoint when you need a lightweight, per-call sentiment score with an explanation — especially in agentic pipelines that pay per use via x402. It is well-suited for classifying short to medium text snippets such as reviews, social posts, or support messages where a score, magnitude, and brief reason are more useful than raw model probabilities. Prefer it over general-purpose LLM calls when you want a structured, consistent output format at a predictable $0.006 USDC cost.
Side effects
- Empty or missing text input returns a validation error
- Extremely short or ambiguous text (e.g. single word) may yield low-confidence or neutral output
- Text in unsupported or mixed languages may produce inaccurate classifications
- Very long text inputs may be truncated or rejected
- Network or upstream model errors return a 5xx response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"sentiment": "positive"
}
}Tags
x402
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