# NetIntel Sentiment Analyze

> 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

## Facts

- Endpoint: POST https://netintel.dev/sentiment/analyze
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-13
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/netintel-sentiment-analyze-c5e25406
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_EL7ZUhh8GvJoGX9reerU6

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability netintel-sentiment-analyze-c5e25406 -d '<json body>'
```

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.'

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "properties": {
  "input": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "type": "string",
     "enum": [
      "POST"
     ]
    },
    "bodyType": {
     "type": "string",
     "enum": [
      "json",
      "form-data",
      "text"
     ]
    },
    "body": {
     "type": "object",
     "properties": {
      "text": {
       "type": "string",
       "description": "The text to analyze sentiment for — a review, message, feedback, or any natural-language text. Max 10000 words or 50KB."
      },
      "aspects": {
       "type": "array",
       "items": {
        "type": "string"
       },
       "description": "Optional list of up to 10 aspects for aspect-based sentiment (e.g. [\"price\", \"service\", \"quality\"]). When provided, sentiment toward each named aspect is also returned."
      }
     },
     "required": [
      "text"
     ]
    }
   },
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "polarity": {
       "type": "string",
       "description": "Overall sentiment: positive, negative, neutral, or mixed"
      },
      "score": {
       "type": "number",
       "description": "Sentiment polarity score from -1.0 (very negative) to 1.0 (very positive)"
      },
      "confidence": {
       "type": "number",
       "description": "Model confidence 0.0-1.0"
      },
      "emotions": {
       "type": "array",
       "items": {
        "type": "string"
       },
       "description": "Detected emotions drawn from joy, anger, sadness, fear, surprise, disgust, trust, anticipation"
      },
      "aspects": {
       "type": "object",
       "description": "Per-aspect sentiment (present only when the aspects param was supplied); each aspect maps to { polarity, score }"
      },
      "service_score": {
       "type": "number",
       "description": "Endpoint health score 0-100 (100 on success) — distinct from the sentiment score"
      },
      "grade": {
       "type": "string",
       "description": "Letter grade A-F derived from service_score"
      },
      "findings": {
       "type": "array",
       "description": "Reserved — currently always [] on success (anal
… (truncated)
```

## Response schema (JSON Schema)

```json
{
 "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
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/netintel-sentiment-analyze-c5e25406/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from netintel.dev](https://www.zero.xyz/host/netintel.dev/llms.txt)