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Text Sentiment Analysis
x402
Text Sentiment Analysis is a paid API for AI agents from bess-x402.onrender.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-30).
Analyzes English text to return a positive/negative/neutral label, compound sentiment score (-1 to 1), word shares, and optionally per-sentence scores with most positive and negative lines.
Price / call$0.002
Success—
StatusUnknown
How this service works
Sentiment of English text (reviews, posts, headlines): positive/negative/neutral label, compound score -1..1 and word shares, optionally per sentence with the most positive and negative lines. Rule-based, instant.
Output
Returns a sentiment label (positive, negative, or neutral), a compound score between -1.0 and 1.0, positive/negative/neutral word share percentages, and optionally a per-sentence breakdown including the most positive and most negative individual sentences in the input.
Example use cases
E-commerce review tone check
“Classify the sentiment of this product review and give me the compound score: 'The battery life is amazing and the design is sleek, but the charging cable broke after two weeks and support was unhelpful.'”
Social media post monitoring
“Is this tweet positive, negative, or neutral? 'Just tried the new downtown branch — food was incredible but wait time was over an hour, totally unacceptable.' Give me the label and a score from -1 to 1.”
News headline mood analysis
“Score the sentiment of each sentence in this earnings announcement paragraph and tell me which sentence is the most negative: 'Revenue hit a record high this quarter. However, operating costs surged unexpectedly. The outlook for next quarter remains uncertain.'”
Technical details
Endpoint
GEThttps://bess-x402.onrender.com/v1/text/sentiment?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need instant, rule-based sentiment classification of English text — reviews, posts, headlines, or support tickets — without the latency or cost of an LLM-based approach. Ideal for high-volume, low-latency pipelines where a compound score and word-share breakdown are sufficient. Prefer it over LLM sentiment when you want deterministic, reproducible results and per-sentence granularity at $0.002 per call.
Side effects
- Missing required 'text' query parameter returns an error
- Non-English text may produce inaccurate or unreliable scores since the model is English-only
- Very short or ambiguous text (e.g. single words) may yield low-confidence or neutral scores
- Extremely long texts may be truncated or slow to process
- Service unavailability on Render's free tier causing 503 errors after cold start
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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