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Vidar Agent – Sentiment Analysis
x402
Vidar Agent – Sentiment Analysis is a paid API for AI agents from lavish-vision-production-bf85.up.railway.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Analyzes sentiment of text, tweets, or market narratives to determine bullish/bearish/neutral tone and emotional signals
Price / call$0.01
Success—
StatusUnknown
How this service works
Sentiment analysis on text, tweets, or market narratives
Output
Returns a sentiment classification (e.g. bullish/bearish/neutral or positive/negative/neutral) along with a confidence score or intensity measure, and potentially key phrases or reasoning driving the sentiment verdict for the input text or narrative.
Example use cases
Monitor social sentiment for trading signals
“Can you check the sentiment of these Twitter posts about Bitcoin's latest rally? I need to know if the overall tone is bullish or bearish so I can adjust my trading strategy accordingly.”
Track community sentiment during token launches
“Analyze the sentiment across these Discord messages and tweets about the new altcoin launch. Are people excited and positive, or skeptical and worried? I want to gauge if there's genuine community interest.”
Assess market narrative shifts in news articles
“Read through these crypto news articles about the Fed's latest announcement and tell me whether the overall sentiment is bullish, bearish, or neutral for crypto markets. What's driving the tone?”
Technical details
Endpoint
POSThttps://lavish-vision-production-bf85.up.railway.app/entrypoints/analyze/invoke?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need crypto- and finance-aware sentiment analysis on tweets, market narratives, or social media text — especially when standard NLP tools lack domain context around token slang, whale behavior, or DeFi terminology. Ideal for pipelines that feed sentiment into trading signals or market monitoring dashboards.
Side effects
- Empty or missing input text returns a validation error
- Extremely long inputs may be truncated or rejected
- Non-English or highly encoded text may yield low-confidence results
- Payment not included or incorrect amount returns HTTP 402
- Ambiguous or context-free text may produce neutral classification with low confidence
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | object |
Tags
x402
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