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Citiview Sentiment & Tone Classifier
x402
Citiview Sentiment & Tone Classifier is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-14).
Classifies text as positive, negative, or neutral with a confidence score and one-line reason; supports batches of up to 20 texts per call.
Price / call$0.003
Success—
StatusUnknown
How this service works
Sentiment and tone classification for text (positive / negative / neutral with confidence, plus a one-line reason). Batch up to 20 texts per call. Also callable as GET with the same fields as query parameters.
Output
For each input text, the endpoint returns a sentiment label (positive, negative, or neutral), a confidence score between 0 and 1, and a one-line reason explaining the classification. Results are returned for each item in the batch in order.
Example use cases
Customer review sentiment triage
“I have a batch of product reviews I need sorted by sentiment — can you classify these as positive, negative, or neutral and give me the confidence and a reason for each: 'Fantastic quality!' | 'Broke after one use.' | 'Decent but overpriced.' | 'Absolutely love this brand!'”
Social media brand monitoring
“Can you analyze the tone of these five tweets about our brand launch and tell me whether each one is positive, negative, or neutral with a confidence score: 'Can't wait to try this!' | 'Looks cheap to me.' | 'Interesting concept.' | 'This is a game changer!' | 'Not impressed at all.'”
Support ticket prioritization
“Run sentiment analysis on these customer support messages so I can prioritize the most negative ones first — tell me the sentiment, confidence, and a one-line reason for each: 'This is unacceptable, I want a refund.' | 'Happy with the resolution, thanks.' | 'Still waiting on my order, getting frustrated.'”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.com/v1/sentiment?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, batch-capable sentiment classification with confidence scores and human-readable explanations — especially if you want to process up to 20 texts in a single API call and need a brief rationale alongside each label. Prefer it over heavier NLP pipelines when cost-efficiency ($0.002 per call for up to 20 texts) and simplicity matter more than fine-grained emotion detection.
Side effects
- Missing 'texts' query parameter returns a validation error
- More than 20 pipe-separated texts in a single call may be rejected
- Very short or ambiguous texts may yield low confidence scores
- Non-English text may produce unreliable sentiment classifications
- Malformed pipe-separated input may cause parsing errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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