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Strale Sentiment Analysis
x402
Strale Sentiment Analysis is a paid API for AI agents from api.strale.io, paid per call via x402, $0.054/call, status unknown (last checked 2026-09-14).
Analyzes the sentiment of a given text string, returning a sentiment classification with supporting details and a cryptographic audit record.
Price / call$0.054
Success—
StatusUnknown
How this service works
The trust layer for AI agents — 250+ independently tested data capabilities across 27 countries. Execute capabilities via REST, MCP, A2A, or x402 micropayments. Every call returns an audit record with cryptographic chain hashing.
Output
Returns a sentiment classification (e.g. positive, negative, neutral) along with confidence scores and a structured audit record that includes a cryptographic chain hash for traceability and trust verification.
Example use cases
Customer review sentiment triage
“Analyze the sentiment of this product review: 'I've been using this app for three months and it keeps crashing every time I try to export — incredibly frustrating, zero stars.'”
Social media mention monitoring
“Tell me whether this tweet about our brand is positive, negative, or neutral: 'Just switched to @BrandX and honestly can't believe I waited this long — game changer.'”
Support ticket emotional tone detection
“Check the sentiment of this support message so I know how urgently to escalate it: 'I have been waiting for a refund for six weeks now and nobody is responding to my emails. This is completely unacceptable.'”
Technical details
Endpoint
GEThttps://api.strale.io/x402/v2/sentiment-analyzeWhen to prefer
Choose this endpoint when you need sentiment analysis with a verifiable audit trail and cryptographic chain hashing — particularly useful in regulated environments, agent pipelines requiring accountability, or any workflow where you need to prove what analysis was run on what data. It is part of a broader 250+ capability trust layer, making it a good fit when you're already using Strale for other data enrichment tasks.
Side effects
- Missing required 'text' query parameter returns a validation error
- Empty or whitespace-only text may return indeterminate or low-confidence results
- Extremely long text inputs may be truncated or rejected
- Non-English text may produce lower accuracy results depending on model support
- Network timeouts under high load
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Tags
x402
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