{"uid":"cap_8V6IugEdGjnRuyB3xW7nz","slug":"text-sentiment-analysis-f29c85bb","name":"Text Sentiment Analysis","description":"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.","url":"https://bess-x402.onrender.com/v1/text/sentiment?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"English text"},"per_sentence":{"type":"boolean","description":"Score each sentence too"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_KDYhyKqt-FUnDWw2mnry9","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Can you check the sentiment of this customer review — 'The product looks great but delivery was a disaster and customer support was useless' — and give me the overall positive/negative/neutral label, the compound score, and break it down sentence by sentence?","exampleUseCases":[{"title":"E-commerce review tone check","prompt":"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.'"},{"title":"Social media post monitoring","prompt":"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."},{"title":"News headline mood analysis","prompt":"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.'"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T15:39:38.754Z","isFirstParty":false,"canonicalSlug":"text-sentiment-analysis-f29c85bb"}