Sentiment Analyzer

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?

Feature	This Actor	Google NLP	AWS Comprehend	GPT-based actors
Price per 1K texts	$2.60	$1.00-2.00	$1.00	$5.00-20.00
External API needed	No	Yes	Yes	Yes
Deterministic results	Yes	Yes	Yes	No
Speed per text	< 5ms	~100ms	~100ms	~1s
Emotion detection	Yes	Partial	No	Yes
Zero-config	Yes	Needs key	Needs key	Needs key
Data privacy	Full	External API	External API	External API

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

Parameter	Type	Default	Description
texts	array	sample texts	Texts to analyze. Plain strings or {id, text, metadata} objects. Max 100K.
sentimentModel	string	"combined"	afinn, vader, or combined (most accurate)
detectEmotions	boolean	true	Detect joy, anger, sadness, fear, surprise, disgust
extractKeyPhrases	boolean	true	Extract key phrases contributing to sentiment
detectLanguage	boolean	true	Detect language of each text
neutralThreshold	number	0.05	Compound score range for neutral classification
onlySentiment	string	null	Filter: positive, negative, neutral, mixed
includeWordScores	boolean	false	Per-word sentiment scores
minimumConfidence	number	0	Minimum confidence threshold

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

Compound Score	Label	Description
0.05 to 1.0	positive	Positive sentiment
-0.05 to 0.05	neutral	No strong sentiment
-1.0 to -0.05	negative	Negative sentiment
Both pos & neg > 0.3	mixed	Contains both positive and negative

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.

Scenario	Texts	Cost
Product reviews batch (500)	500	$1.30
Twitter mentions (5,000)	5,000	$13.00
Customer feedback (20,000)	20,000	$52.00
Monthly monitoring (100,000)	100,000	$260.00

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.