{"slug":"netintel-dev-sentiment-analyze-dfccf2","title":"Sentiment analysis API — analyze sentiment of text and get a text sentiment scor","host":"netintel.dev","method":"POST","resource":"https://netintel.dev/sentiment/analyze","category":"other","description":"Sentiment analysis API — analyze sentiment of text and get a text sentiment score in one call: classifies positive / negative / neutral / mixed polarity with a -1 to +1 sentiment score, plus emotion detection in text (joy, anger, sadness, fear, surprise, disgust, trust, anticipation). Aspect-based s","price_listed":0.002,"price_asked":0.002,"state":"answering","state_label":"Answering","checks_7d":1,"answered_7d":1,"latency_ms_median":925,"reported_calls_30d":2,"reported_payers_30d":2,"networks":["eip155:8453","solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp"],"badge":"unverified","paid_checks_7d":0,"paid_ok_7d":0,"example_input":{"body":{"aspects":["food","service","price"],"text":"The food was absolutely delicious and the staff were so friendly, but the prices were a bit steep."},"bodyType":"json","method":"POST","type":"http"},"output_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","properties":{"input":{"additionalProperties":false,"properties":{"body":{"properties":{"aspects":{"description":"Optional list of up to 10 aspects for aspect-based sentiment (e.g. [\"price\", \"service\", \"quality\"]). When provided, sentiment toward each named aspect is also returned.","items":{"type":"string"},"type":"array"},"text":{"description":"The text to analyze sentiment for — a review, message, feedback, or any natural-language text. Max 10000 words or 50KB.","type":"string"}},"required":["text"],"type":"object"},"bodyType":{"enum":["json","form-data","text"],"type":"string"},"method":{"enum":["POST"],"type":"string"},"type":{"const":"http","type":"string"}},"required":["type","method","bodyType","body"],"type":"object"},"output":{"properties":{"example":{"properties":{"aspects":{"description":"Per-aspect sentiment (present only when the aspects param was supplied); each aspect maps to { polarity, score }","type":"object"},"confidence":{"description":"Model confidence 0.0-1.0","type":"number"},"emotions":{"description":"Detected emotions drawn from joy, anger, sadness, fear, surprise, disgust, trust, anticipation","items":{"type":"string"},"type":"array"},"findings":{"description":"Reserved — currently always [] on success (analysis errors surface as 502s, never findings)","type":"array"},"grade":{"description":"Letter grade A-F derived from service_score","type":"string"},"polarity":{"description":"Overall sentiment: positive, negative, neutral, or mixed","type":"string"},"score":{"description":"Sentiment polarity score from -1.0 (very negative) to 1.0 (very positive)","type":"number"},"service_score":{"description":"Endpoint health score 0-100 (100 on success) — distinct from the sentiment score","type":"number"}},"type":"object"},"type":{"type":"string"}},"required":["type"],"type":"object"}},"required":["input"],"type":"object"},"history":[{"day":"2026-09-24","reachable":true,"status":402,"valid_402":true,"asked_usdc":0.002,"price_match":true,"latency_ms":925,"error":null}],"description_full":"Sentiment analysis API — analyze sentiment of text and get a text sentiment score in one call: classifies positive / negative / neutral / mixed polarity with a -1 to +1 sentiment score, plus emotion detection in text (joy, anger, sadness, fear, surprise, disgust, trust, anticipation). Aspect-based sentiment and opinion mining for customer feedback analysis — analyze reviews, support tickets, social posts, chat messages. Via Claude Haiku.","last_updated":"2026-09-08T14:39:33.61Z","schemes":["exact"]}