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Sentiment Scoring API (twitter-roberta-base-sentiment)
x402
Sentiment Scoring API (twitter-roberta-base-sentiment) is a paid API for AI agents from srv1553682.hstgr.cloud, paid per call via x402, $0.0035/call, status unknown (last checked 2026-09-15).
Scores one or up to 16 texts per call as positive, neutral, or negative with confidence values using the twitter-roberta-base-sentiment model.
Price / call$0.0035
Success—
StatusUnknown
How this service works
Sentiment scoring for caller-provided text: positive/neutral/negative with confidence. Batch up to 16 texts per call. Model: twitter-roberta-base-sentiment. Always-on, verified onchain settlement.
Output
Returns a sentiment label (positive, neutral, or negative) and a confidence score (0–1) for each submitted text. When batching, results are returned as an array aligned to the input order, supporting up to 16 texts per call.
Example use cases
Social media brand monitoring
“Analyze the sentiment of these 10 recent tweets mentioning our brand and tell me which are positive, which are negative, and how confident you are for each.”
Customer review triage
“Score the sentiment of this customer review with a confidence level — positive, neutral, or negative: 'The packaging was fine but the product stopped working after two days.'”
Support ticket tone detection
“Check whether this support message is positive, neutral, or negative so I know how urgently to escalate it: 'I've been waiting three weeks and nobody has responded to my emails.'”
Technical details
Endpoint
POSThttps://srv1553682.hstgr.cloud/nlp/api/sentimentWhen to prefer
Choose this endpoint when you need fast, lightweight sentiment classification (positive/neutral/negative) with confidence scores on short to medium texts — especially social media posts, reviews, or support tickets — and need batch throughput up to 16 texts per call with per-call micropayment billing rather than a subscription. The twitter-roberta-base-sentiment model is specifically optimized for informal, short-form text similar to tweets, making it a strong fit for social and conversational content over formal documents.
Side effects
- Batch size exceeds 16 texts — request rejected
- Empty or missing both 'text' and 'texts' fields — validation error
- Text too long for the model's context window — truncation or error
- Payment not settled on-chain — 402 response before processing
- Model inference timeout on very long or complex inputs
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | single text to score |
| texts | array | batch of texts (max 16) |
Tags
x402
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