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Inference402 Sentiment Analysis
x402
Inference402 Sentiment Analysis is a paid API for AI agents from inference402.worlddata402.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-19).
Analyzes a text string and returns a sentiment label (positive/negative/neutral) with a numeric confidence score, powered by Llama 3.1 8B, paid per-call via USDC on Base.
Price / call$0.002
Success—
StatusUnknown
How this service works
Pay-per-call AI inference for agents: chat (Llama 3.3 70B / 3.1 8B), structured JSON, summarize, translate (incl. Egyptian Arabic), extract, classify, sentiment, keywords, rewrite, embeddings, moderation, vision and speech-to-text. USDC on Base mainnet over x402. No API key, no account.
Output
Returns a JSON object with three fields: 'sentiment' (a label such as 'positive', 'negative', or 'neutral'), 'score' (a float between 0 and 1 indicating confidence), and 'model' (the specific Llama model variant used, e.g. '@cf/meta/llama-3.1-8b-instruct-fp8').
Example use cases
Customer review sentiment triage
“I've got a batch of product reviews coming in — can you analyze the sentiment of this one and tell me if it's positive, negative, or neutral with a confidence score: 'Great quality but took three weeks to arrive, very disappointing'?”
Social media tone monitoring
“Check the sentiment of this tweet for me and give me a score: 'Can't believe how bad the support experience was — waited 2 hours and got zero help. Never again.'”
Support ticket prioritization
“I need to know if this support message is negative so I can escalate it — can you run sentiment analysis on: 'I've been waiting five days for a response and my issue still isn't resolved, this is unacceptable'?”
Technical details
Endpoint
GEThttps://inference402.worlddata402.workers.dev/v1/sentiment?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, pay-as-you-go sentiment analysis with no API key or account setup, paying $0.002 USDC per call on Base mainnet. Ideal for agents that need occasional or burst sentiment classification without committing to a subscription, or for autonomous agents that can self-fund inference via x402. Prefer over heavier NLP pipelines when a fast scalar sentiment score (label + confidence) is sufficient and you do not need entity-level or aspect-level breakdown.
Side effects
- Text exceeds 8000 character limit — request rejected
- Payment not received or USDC balance insufficient — 402 Payment Required response
- Empty or missing 'text' parameter — likely returns an error or unexpected sentiment
- Model inference timeout on very dense or complex text
- Non-English text may yield lower accuracy sentiment scores (model is optimized for English)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "@cf/meta/llama-3.1-8b-instruct-fp8",
"score": 0.82,
"sentiment": "positive"
}
}Tags
x402
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