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Sift Web Tools Text Classifier
x402
Sift Web Tools Text Classifier is a paid API for AI agents from sift.srv1996466.hstgr.cloud, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-20).
Analyzes text (up to 4000 chars) for language, sentiment, emotions, topics, toxicity, and optionally sorts it into user-defined labels
Price / call$0.005
Success—
StatusUnknown
How this service works
Sentiment, emotions, topics and toxicity of text, optionally sorted into your own labels. Analyses up to 4000 characters: language, sentiment and score (-1 to 1), up to 3 emotions, topics, toxicity and a one-line summary. Pass labels=a,b,c to sort the text into your own categories with a score for each.
Output
A JSON object containing: detected language, overall sentiment label (positive/negative/neutral), a numeric sentiment score from -1 to 1, up to 3 emotions present in the text, relevant topics, a toxicity level (e.g. none/mild/high), a one-line summary, and if labels were provided, a score for each custom label indicating how well the text fits that category.
Example use cases
Moderation pipeline for user comments
“Check this forum post for toxicity and flag any harmful emotions before it goes live: 'People like you should just disappear, nobody wants your opinion here.'”
Support ticket routing by custom label
“Classify this support message into one of these categories — 'billing', 'technical', or 'general' — and give me a sentiment score so I know how urgent it is: 'I was charged twice this month and nobody has responded to my emails for a week.'”
Social media brand sentiment monitoring
“Analyze this tweet about our brand for sentiment, the top emotions it conveys, and give me a one-line summary: 'Honestly blown away by how fast the team resolved my issue — best customer support I've ever experienced!'”
Technical details
Endpoint
GEThttps://sift.srv1996466.hstgr.cloud/v1/classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined multi-signal text analysis in a single call — sentiment score, emotions, topics, toxicity, language detection, and a summary — especially when you also want to sort text into your own custom label taxonomy. It is well-suited for content moderation, customer feedback triage, and brand monitoring pipelines where multiple NLP signals are needed simultaneously without chaining multiple API calls.
Side effects
- Text exceeds 4000 characters — the endpoint may truncate or reject the input
- Missing required 'text' query parameter — returns an error
- Ambiguous or very short text may yield low-confidence or generic sentiment scores
- Malformed labels parameter (not comma-separated) may be ignored or cause unexpected results
- Non-English text may have reduced accuracy for emotion and topic detection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"label": "complaint",
"language": "English",
"toxicity": "none",
"sentiment": "negative",
"sentiment_score": -0.7
}
}Tags
x402
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Zero review prompt (fires after every successful call)
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