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NetIntel Sentiment Analyze
x402
NetIntel Sentiment Analyze is a paid API for AI agents from netintel.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Analyzes text for sentiment, returning a grade, polarity score, aspect-level breakdown, emotions, and confidence level
Price / call$0.002
Success—
StatusUnknown
How this service works
Sentiment analysis API — analyze sentiment of text and get a text sentiment score in one call: classifies positive / negative / neutral / mixed polarity with a -1 to +1 sentiment score, plus emotion detection in text (joy, anger, sadness, fear, surprise, disgust, trust, anticipation). Aspect-based sentiment and opinion mining for customer feedback analysis — analyze reviews, support tickets, social posts, chat messages. Via Claude Haiku.
Output
Returns a JSON object with an overall letter grade (e.g. 'A'), a numeric sentiment score, per-aspect scores and polarity labels (e.g. food, price, service), a list of detected emotions (e.g. joy, trust), overall polarity (positive/negative/mixed), a confidence score (0–1), and a service_score integer. Also includes a findings array for any notable observations.
Example use cases
Example prompt
“Can you analyze the sentiment of this restaurant review and break it down by aspect — food, price, and service — with an overall grade and confidence score? The review is: 'The pasta was absolutely delicious and the staff were wonderful, but we paid way too much for the portion sizes.'”
Technical details
Endpoint
POSThttps://netintel.dev/sentiment/analyzeWhen to prefer
Use this endpoint when you need fine-grained, aspect-level sentiment analysis with emotion detection and a graded output — especially for review text covering multiple dimensions like food, price, and service. Prefer this over generic sentiment APIs when you need structured aspect breakdowns and confidence scoring rather than just a simple positive/negative classification.
Side effects
- Empty or missing text input returns a validation error
- Text that is too short or ambiguous may yield low confidence scores
- Non-English text may degrade aspect detection accuracy
- Network timeout on very long text inputs
- Payment failure (402) if USDC balance is insufficient on Base mainnet
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"grade": "A",
"score": 0.2,
"aspects": {
"food": {
"score": 0.9,
"polarity": "positive"
},
"price": {
"score": -0.4,
"polarity": "negative"
},
"service": {
"score": 0.8,
"polarity": "positive"
}
},
"emotions": [
"joy",
"trust"
],
"findings": [],
"polarity": "mixed",
"confidence": 0.86,
"service_score": 100
}
}Tags
x402
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