Text Sentiment & Aspect Analysis

omnial/x402-sentiment-analyze

Given a piece of text (a review, a comment, feedback), returns an overall sentiment label with confidence and a positive/negative/neutral score breakdown, plus aspect-level sentiment for individual topics mentioned in the text (e.g. product quality vs. customer service) with the exact text span that supports each one. Your input is sent to an outside service for processing; remove secrets and personal data first.

activeper callv5

Price

Up to $0.0811 per call; you pay what the call used

You pay the price shown. If a call turns out to cost more than its quote, you pay the actual price, capped at twice the quote. No monthly minimum. Top-ups start at $5.00.

Data source

Anthropic

Generated by Anthropic's Claude from the inputs you provide.

Use it

Your AI assistant does the work: it finds this tool, shows you the price first, runs it, and you are charged when the run finishes. There is nothing to set up for this tool in particular.

Connect once and your AI can find and run this tool, and any other it needs.

Install it Create an account

For developers: the calls your assistant makes

agent

// 1. search: find the tool
omnial_search({ query: "what you need" })

// 2. inspect: price and input schema
omnial_inspect({ tool: "omnial/x402-sentiment-analyze" })

// 3. learn: read the usage guide at runtime
omnial_learn({ tool: "omnial/x402-sentiment-analyze" })

// 4. run: price the exact call first, then run it
omnial_execute({ tool: "omnial/x402-sentiment-analyze", input: { /* see input_schema */ }, dry_run: true })
omnial_execute({ tool: "omnial/x402-sentiment-analyze", input: { /* ... */ } })

// 5. settle: the call is charged when it finishes, nothing to do

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Pricing

Every charge this tool can incur.

Charge	Price
Per call	$0.0811

Updated: Oct 27, 2026

Input and output schemas

Input schema

input_schema.json

{
  "type": "object",
  "required": [
    "text"
  ],
  "properties": {
    "text": {
      "type": "string",
      "description": "Text to analyze"
    }
  },
  "additionalProperties": false
}

Output schema

output_schema.json

{
  "type": "object",
  "properties": {
    "scores": {
      "type": "object",
      "properties": {
        "neutral": {
          "type": "number"
        },
        "negative": {
          "type": "number"
        },
        "positive": {
          "type": "number"
        }
      },
      "description": "Positive/negative/neutral breakdown, observed summing to 1 on both calls.",
      "additionalProperties": true
    },
    "aspects": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "aspect": {
            "type": "string",
            "description": "Short topic label, e.g. 'food quality'."
          },
          "sentiment": {
            "type": "string",
            "description": "This aspect's own sentiment label."
          },
          "text_span": {
            "type": "string",
            "description": "The exact substring of the input text that supports this aspect."
          }
        },
        "additionalProperties": true
      },
      "description": "One entry per distinct topic the text discusses (2 entries on the product review, 3 on the mixed restaurant feedback) -- not a fixed count."
    },
    "summary": {
      "type": "string",
      "description": "A one-to-two sentence plain-language summary of the analysis."
    },
    "sentiment": {
      "type": "string",
      "description": "Overall label observed as 'positive' and 'mixed' in testing; the endpoint's own description also documents 'negative' and 'neutral' as possible values, neither observed yet."
    },
    "confidence": {
      "type": "number",
      "description": "0-1. 0.98 on an unambiguous positive review, 0.92 on a mixed one."
    },
    "detected_language": {
      "type": "string",
      "description": "e.g. 'English'. Observed as a full language name, not an ISO code."
    }
  },
  "description": "The sentiment record.",
  "additionalProperties": true
}