Sentiment Analysis Consensus: Aggregate Scores Across Models
Single-model sentiment analysis is unreliable. Multi-model consensus delivers 95%+ accuracy with confidence scores.
Why Single-Model Sentiment Fails
Individual AI models interpret sarcasm, cultural context, and nuanced opinions differently. One model might classify "Not bad at all!" as negative while another correctly identifies it as positive. Consensus eliminates these inconsistencies.
How Consensus Scoring Works
Each text is analyzed by 3+ models independently. Results are aggregated using a weighted voting system that accounts for each model's historical accuracy on similar content.
import vincony
client = vincony.Client(api_key="YOUR_API_KEY")
texts = [
"This product exceeded my expectations. Absolutely worth the price.",
"I guess it works, but I expected more for what I paid.",
"Not bad at all! Surprisingly good quality.",
"The shipping was fast but the product fell apart in a week."
]
results = client.tools.sentiment(
texts=texts,
models=["gpt-4o", "claude-sonnet-4", "gemini-2-flash"],
output=["sentiment", "confidence", "emotion", "aspect_sentiment"]
)
for r in results:
print(f"Text: {r.text[:50]}...")
print(f" Sentiment: {r.sentiment} ({r.confidence}% confidence)")
print(f" Emotions: {r.emotions}") # {"joy": 0.7, "surprise": 0.2}
print(f" Model agreement: {r.agreement}%")Aspect-Based Sentiment
Don't just get overall sentiment — break it down by aspect. A product review might be positive about quality but negative about price. Aspect sentiment reveals the full picture.
Real-Time Monitoring
Set up continuous sentiment monitoring for social media mentions, support tickets, or review platforms. Get alerted when sentiment drops below your threshold.
# Set up sentiment monitoring
monitor = client.tools.sentiment.monitor(
source="twitter",
query="@vincony OR #vincony",
alert_threshold={"negative_pct": 30},
check_interval="1h",
notify=["slack", "email"]
)Pricing
500 analyses/month on Free. Unlimited with monitoring and aspect analysis on Pro and Enterprise.
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