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Pocket Network Sentiment Classification
x402
Pocket Network Sentiment Classification is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-30).
Scores a text as positive, negative, or neutral using an AFINN-derived lexicon, returning a numeric score and matched positive/negative word hits
Price / call$0.005
Success—
StatusUnknown
How this service works
Lexicon-based sentiment scoring: label a text positive, negative or neutral with a score and the positive and negative word hits behind it (AFINN-derived word list, not a trained model). POST /v1/sentiment with {text}. Deterministic. Pay per request in USDC; no account, no API key.
Output
A JSON object containing a sentiment label (positive, negative, or neutral), a numeric sentiment score, and lists of matched positive and negative words from the AFINN-derived lexicon that contributed to the score.
Example use cases
Customer review tone screening
“Classify this product review as positive, negative, or neutral and show me which specific words pushed the score in each direction: 'Battery life is outstanding but the screen is annoyingly dim and hard to read outdoors.'”
Social media comment moderation
“Tell me the sentiment polarity — positive, negative, or neutral — of this tweet along with the word hits behind it: 'Absolutely love the new update! The app crashes way less and feels snappier than before.'”
Support ticket prioritization
“Run sentiment scoring on this customer support message so I know how negative it is: 'I have been waiting three weeks for a refund and nobody has responded to my emails — this is unacceptable.'”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/sentiment-classification?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, deterministic, explainable sentiment scoring without training a model — especially when reproducibility matters (same input always yields the same output). Ideal for lightweight pipelines that need word-level attribution showing exactly which terms drove the score. Prefer it over ML-based sentiment APIs when latency, transparency, and per-call pay-as-you-go pricing (no API key or subscription) are priorities. Not ideal for nuanced sarcasm detection or non-English text.
Side effects
- Empty or missing text field returns an error or zero-score result
- Non-English text may yield inaccurate scores since the AFINN word list is English-centric
- Very short or single-word input may produce a neutral label due to limited lexical matches
- Payment failure (insufficient USDC balance) results in a 402 response blocking the call
- Slang, sarcasm, or irony is not detected since this is lexicon-based, not a trained model
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | The text to score. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"portal": {
"serviceId": "sentiment-classification",
"provenance": "third-party-supplier",
"schemaCheck": "passed"
}
}
}Tags
x402
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