Sentiment Detection Agent

The Sentiment Detection Agent analyzes text to determine the emotional tone and attitude expressed, providing insights into user satisfaction, brand perception, and content impact. It uses advanced natural language processing to classify sentiment on a continuous scale from negative to positive.

Sentiment Detection Agent interface and configuration

Component Inputs

Input Text: The text content to be analyzed for sentiment
Example: "I absolutely love this product! It exceeds all my expectations."
Sentiment Threshold: The threshold to classify sentiment intensity
Range: -1.0 (extremely negative) to 1.0 (extremely positive)
Default: ±0.3 (values between -0.3 and 0.3 considered neutral)

Component Outputs

Processed Text: The analyzed content with potential sentiment markers
Safety Status: Overall assessment based on sentiment analysis
Values: Safe (positive/neutral), Warning (moderately negative), Unsafe (highly negative)
Risk Score: Numerical evaluation of negative sentiment risk
Scale: 0.0 (no risk) to 1.0 (high risk)

Sentiment Classification

Positive

Satisfaction
Happiness
Enthusiasm
Gratitude
Trust

Neutral

Factual
Informative
Objective
Balanced
Uncertain

Negative

Frustration
Disappointment
Anger
Dissatisfaction
Concern

How It Works

The Sentiment Detection Agent employs a fine-tuned transformer model specifically trained on diverse sentiment datasets. It analyzes the semantic and contextual elements of text to determine emotional tone, providing a continuous score from negative to positive.

Analysis Capabilities

Continuous sentiment scoring (-1.0 to 1.0)
Detection of mixed or conflicting sentiments
Recognition of sarcasm and subtle emotional cues
Language-specific sentiment nuances
Context-aware interpretation
Domain adaptation for industry-specific terminology

Use Cases

Customer Feedback Analysis: Monitor product reviews and support interactions for satisfaction levels
Brand Monitoring: Track sentiment across social media and news mentions
Content Filtering: Flag extremely negative content that might require moderation
Market Research: Analyze open-ended survey responses and interviews
Customer Support Prioritization: Route urgent negative feedback to appropriate teams

Implementation Example

const sentimentAnalyzer = new SentimentDetector({
  threshold: 0.3,  // Define threshold for positive/negative classification
  language: 'en',  // Specify language for analysis
  model: 'roberta-sentiment'  // Select model variant
});

const inputText = "I'm really impressed with your customer service. +
 The agent was so helpful!";
const result = sentimentAnalyzer.analyze(inputText);

// Output:
// {
//   processedText: "I'm really impressed with your customer service. +
//   The agent was so helpful!",
//   sentiment: 0.87,  // Highly positive
//   safetyStatus: "Safe",
//   riskScore: 0.05,
//   sentimentBreakdown: {
//     positive: 0.89,
//     neutral: 0.11,
//     negative: 0.00
//   }
// }

Useful Resources

Best Practices

Adjust sentiment thresholds based on your specific application and audience expectations
Consider industry-specific context when interpreting sentiment scores
Combine with other security agents like Toxicity Detection for comprehensive content analysis
Use sentiment trends over time rather than individual scores for strategic decisions
Implement human review for borderline cases where sentiment is ambiguous

Documentation