Sentiment Analyzer

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Batch text sentiment analysis with emotion detection, key phrase extraction, and language detection. Processes up to 100,000 texts using offline NLP — no external AI API needed, fast, deterministic, and private.

Why Use This Actor?

At $2.60/1K texts, this actor is 3-13x cheaper than LLM-based alternatives while being 200x faster and fully deterministic. Your text data never leaves Apify infrastructure.

How to Use

Zero-config (just provide texts):

{
"texts":[
"This product is amazing! Best purchase ever.",
"Terrible service, waited 2 hours.",
"The package arrived on Tuesday."
]
}

With ID and metadata:

{
"texts":[
{"id":"review-123","text":"Love this product!","metadata":{"source":"amazon"}}
]
}

Review analysis pipeline (filter negative reviews):

{
"texts":["... your review texts ..."],
"onlySentiment":"negative",
"extractKeyPhrases":true
}

Input Configuration

Output Format

{
"id":"review-123",
"text":"This product is amazing!",
"textLength":25,
"language":{"detected":"en","name":"English","confidence":0.9},
"sentiment":{
"label":"positive",
"compound":0.8721,
"positive":0.8721,
"negative":0.0,
"neutral":0.1279,
"confidence":0.9721
},
"emotions":{
"joy":0.8,"anger":0.0,"sadness":0.0,
"fear":0.0,"surprise":0.1,"disgust":0.0,
"dominant":"joy"
},
"keyPhrases":[
{"phrase":"amazing","sentiment":1.0,"importance":0.9}
],
"analyzedAt":"2026-03-11T12:00:00.000Z"
}

Sentiment Score Interpretation

Tips and Advanced Usage

Choosing the right model:

• combined (default) — Best accuracy, uses AFINN + VADER ensemble
• vader — Better for social media with emojis, slang, caps, punctuation
• afinn — Fastest, purely lexicon-based

Adjusting neutral threshold:

• Default 0.05 works well for most use cases
• Increase to 0.15-0.20 for stricter positive/negative classification

Handling sarcasm: Lexicon-based analysis cannot reliably detect sarcasm ("Oh great, another delay"). For sarcasm, consider LLM-based tools.

Multi-language: Works best with English. 20+ languages supported via translated lexicons with lower accuracy.

Pricing

Pay-Per-Event: $0.0026 per text analyzed ($2.60 per 1,000 texts)

Pricing includes all platform compute costs — no hidden fees.

Related Actors

• Finance News Scraper — Pair with sentiment for financial signal detection
• Google News Scraper — News to analyze with sentiment

FAQ

How accurate is the sentiment analysis?

For standard review and social media text, lexicon-based approaches achieve 75-85% accuracy vs LLM methods (80-90%). For most business use cases, this difference is negligible at a 10x cost saving.

Does it detect sarcasm?

No. Sarcasm detection requires contextual understanding that lexicon-based NLP cannot reliably provide. Document this limitation for your users.

What languages are supported?

English is the primary language with highest accuracy. 20+ other languages are supported via translated lexicons with moderate accuracy.

Can I analyze social media posts with emojis?

Yes — emoji sentiment mapping is built in. Common emojis are mapped to sentiment values.

What's the difference between AFINN and VADER models?

AFINN: lexicon-based word scoring, fast and simple. VADER: rule-based with negation handling, emphasis (CAPS, !!!), and emoji support — better for social media.

How does emotion detection work?

Emotion detection uses a word-emotion association lexicon. Each word is mapped to one of 6 emotions. The dominant emotion is the one most frequently detected in the text.