📊 Sentiment Analyzer — Fast VADER Scoring for Reviews & Social
Pricing
from $20.00 / 1,000 results
📊 Sentiment Analyzer — Fast VADER Scoring for Reviews & Social
Score sentiment on reviews, social posts, support tickets, and any English text. Uses VADER (rule-based, deterministic, optimized for informal short text). Fast, predictable cost, no API keys or LLM fees. Best for high-volume scoring and social media monitoring.
Pricing
from $20.00 / 1,000 results
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Score sentiment on reviews, social posts, support tickets, survey responses, news articles, and any English text. Powered by VADER (Valence Aware Dictionary for sEntiment Reasoning) — a rule-based, lexicon-driven sentiment analyzer originally developed at Georgia Tech, specifically tuned for short informal text.
Best when: you need fast, deterministic, predictable-cost sentiment scoring at scale. No API keys, no LLM rate limits, no per-token billing surprises.
When to use this actor
- High-volume review mining — score thousands of Amazon/Yelp/Google reviews per run
- Social media monitoring — Twitter/X, Reddit, Instagram captions
- Support ticket triage — flag negative tickets for priority handling
- Survey response analysis — open-ended NPS / CSAT feedback
- Brand monitoring dashboards — daily/hourly sentiment trend tracking
- Any workflow where deterministic, repeatable scores matter more than nuance
When NOT to use this actor
VADER is excellent at short, opinion-rich, informal text. It's weaker at:
- Sarcasm and irony — "Oh great, another delay" reads as positive to VADER
- Long-form analytical text — academic papers, technical documents
- Multilingual content — VADER is English-only out of the box
- Domain-specific jargon — medical, legal, financial vocabulary may be misscored
- Contextual understanding — VADER reads sentence-by-sentence, not paragraph-aware
For LLM-grade nuance on these cases, consider a transformer-based actor (we may build one in the future at a premium tier).
What you get per result
Each text input returns:
| Field | Description |
|---|---|
compound_score | Single normalized score from -1.0 (most negative) to +1.0 (most positive) |
positive, neutral, negative | Proportion breakdown summing to 1.0 |
label | One of positive / neutral / negative (threshold: ±0.05) |
confidence | Distance of compound from 0 (proxy for opinion strength) |
text_excerpt | First 200 chars of the input for reference |
Pricing
Pay-per-event: $0.02 per sentiment score + $0.00005 actor start. No monthly minimum, no API key fees, no LLM overhead. 1,000 sentiment scores = $20.
Compare to:
- OpenAI gpt-4o-mini sentiment: ~$0.0002/score in API fees alone, plus infrastructure
- AWS Comprehend sentiment: $0.0001/unit (cheaper per call) but requires AWS setup and uses neural models
- Brand24/Mention/Awario: $49-499/mo flat (includes monitoring + dashboards, not just scoring)
This actor is best-in-class for deterministic, transparent, audit-friendly sentiment scoring where you don't need an LLM and don't want to manage an AWS account. If you need LLM-grade interpretation or audit traceability beyond what VADER offers, pick a different tool.
Example use cases
- E-commerce review mining: feed 10K Amazon/Yelp reviews → get daily aggregate sentiment trend by product SKU
- Social listening pipeline: pipe Twitter/X mentions → score each → alert when 7d rolling sentiment crosses threshold
- Support ticket triage: feed Zendesk/Intercom tickets → auto-tag negative ones for priority queue
- NPS comment scoring: parse open-ended survey feedback into structured sentiment counts
- Multi-source brand monitor: combine outputs from
nexgendata/reddit-scraper,nexgendata/yelp-business-scraper,nexgendata/trustpilot-review-scraper, then run all through this actor for unified sentiment
Honest credit
VADER was created by C.J. Hutto and Eric Gilbert at Georgia Tech. Original paper: "VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text" (2014). This actor packages VADER for batch scoring on Apify — the underlying scoring model is the public vaderSentiment Python library.
Cross-links to related NexGenData actors
- Reddit Scraper — pull comments to score
- Yelp Business Scraper — review mining feed
- Trustpilot Review Scraper — review mining feed
- Hacker News Scraper — comment sentiment over time
- AP News Scraper — headline sentiment tracking
NexGenData — built on Apify. thenextgennexus.com
