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KiteEndpoints.ai - News Sentiment Analysis
x402
KiteEndpoints.ai - News Sentiment Analysis is a paid API for AI agents from kite-endpoints.vercel.app, paid per call via x402, $0.08/call, status unknown (last checked 2026-09-15).
Analyzes the sentiment of news articles or headlines, returning positive, negative, or neutral classification with confidence signals
Price / call$0.08
Success—
StatusUnknown
How this service works
400+ paid API endpoints. Social media, AI, crypto, finance, weather, ecommerce, tools. Pay per request with USDC on Base chain.
Output
Returns a sentiment classification (positive, negative, or neutral) for the submitted news content, typically accompanied by a confidence score or sentiment intensity value. The response is structured as a result object containing the classification data.
Example use cases
Financial news sentiment for trading signals
“I just pulled today's top 5 headlines about Apple — can you run sentiment analysis on each one and tell me whether the overall news tone is bullish or bearish?”
Crypto market mood monitoring
“Here's a news snippet about Bitcoin's latest price drop: 'Bitcoin falls 15% amid regulatory crackdown fears.' Analyze the sentiment and tell me if it's negative, and how strongly negative it is.”
Brand reputation tracking from press
“I need to know if this press release about our company is being framed positively or negatively — can you analyze the sentiment: 'Acme Corp faces scrutiny over data privacy practices as regulators open formal investigation.'”
Technical details
Endpoint
POSThttps://kite-endpoints.vercel.app/api/sentiment/newsWhen to prefer
Choose this endpoint when you need quick, pay-per-use sentiment scoring on news content without committing to a subscription NLP service. It is well-suited for agents that sporadically analyze financial or general news headlines and want per-request USDC billing on Base chain rather than monthly API subscriptions. Prefer alternatives with richer schemas if you need entity-level sentiment, aspect-based analysis, or domain-specific financial NLP models.
Side effects
- Empty or missing news text input returns an error or ambiguous result
- Extremely short or ambiguous headlines may yield low-confidence or unreliable classifications
- Non-English news content may not be accurately classified if the model is English-only
- Rate limiting or payment failures via x402/USDC may block the request
- Generic schema may not surface granular sub-sentiment categories (e.g., fear, excitement)
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| result | object | API response data |
Tags
x402
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