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research.topic-sentiment-analyzer
x402
research.topic-sentiment-analyzer is a paid API for AI agents from api.craigmbrown.com, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).
Maps opinion and sentiment for a named topic across social, expert, and community channels, returning per-channel scores rather than a single blended number.
Price / call$0.03
Success—
StatusUnknown
How this service works
Opinion and sentiment mapping across social, expert, and community channels for a named topic, scored per-channel rather than one blended number. Settlement proof: ProofOfSettledOutcome (kind 30120, data/proof_settled_outcomes.jsonl). Same methodology as oracle.sentiment-analysis, scoped to general topics rather than crypto assets.
Output
Returns a JSON object with a text summary of overall sentiment, an array of findings describing per-channel sentiment scores and notable opinions, and an array of recommendations — each channel scored independently rather than blended into a single metric. Powered by BlindOracle via the BlindOracle backend.
Example use cases
Policy perception across audiences
“Give me a per-channel sentiment breakdown on 'remote work mandates' — I want to know separately what social media, experts, and community forums think about it, not a single average score.”
Brand topic sentiment by channel
“Analyze public sentiment around 'plant-based meat' across social, expert, and community channels so I can see where opinion is strongest for or against it.”
Research divergence between experts and public
“I'm writing a report on AI regulation — can you map out how sentiment differs between social media voices, expert commentators, and online communities so I can spot where they disagree?”
Technical details
Endpoint
POSThttps://api.craigmbrown.com/v1/services/research.topic-sentiment-analyzerWhen to prefer
Choose this endpoint when you need per-channel sentiment breakdowns for general topics (non-crypto) rather than a single blended sentiment score. It is preferable over generic sentiment APIs when the user needs to compare social, expert, and community opinion separately, and over crypto-specific sentiment endpoints when the subject is a general topic rather than a financial asset or token.
Side effects
- Vague or ambiguous topic prompt may produce low-signal results
- Topic too niche for sufficient channel coverage may return sparse findings
- Prompt missing a clear subject may yield generic or off-target analysis
- Rate limiting or payment failure may prevent call completion
- Response may not cover all channels equally if data is sparse for some sources
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prompt | string |
Output
Schema · Response
{
"type": "json",
"schema": {
"type": "object",
"properties": {
"summary": {
"type": "string"
},
"findings": {
"type": "array",
"items": {
"type": "string"
}
},
"recommendations": {
"type": "array",
"items": {
"type": "string"
}
}
}
},
"example": {
"summary": "..."
}
}Tags
x402
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