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glossary
What is Sentiment Analysis?
AI technology that detects emotional tone and attitude in text or speech to understand how customers feel.
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Sentiment analysis is an AI capability that identifies the emotional tone expressed in text or speech. In customer service, it helps detect frustrated customers, prioritize urgent cases, and adapt responses to emotional context.
Types of Sentiment
- Positive: Satisfaction, appreciation, happiness
- Negative: Frustration, anger, disappointment
- Neutral: Factual, informational, no strong emotion
- Mixed: Both positive and negative elements
How Sentiment Analysis Works
- Text processing: Message is analyzed for emotional signals
- Feature detection: Words, phrases, and patterns identified
- Classification: Overall sentiment determined
- Intensity scoring: Strength of emotion measured
Sentiment Signals
AI detects sentiment through:
- Word choice: Terrible vs disappointing vs fine
- Punctuation: Exclamation marks, caps
- Phrases: I am so frustrated with...
- Tone shifts: Changes within conversation
Applications in Customer Service
- Priority routing: Escalate angry customers faster
- Response adaptation: Match tone to customer mood
- Quality monitoring: Track customer satisfaction trends
- Agent coaching: Identify challenging interactions
- Churn prediction: Spot at-risk customers
Accuracy Considerations
- Sarcasm: Difficult to detect
- Cultural differences: Expression varies by culture
- Context: Same words mean different things
- Domain: Industry-specific sentiment patterns
Maven AGI Difference: Our AI detects sentiment in real-time and adapts responses accordingly. Frustrated customers get empathetic, expedited handling. Happy customers get efficient resolution. This emotional intelligence contributes to improved CSAT scores.
Book a demo to see sentiment-aware AI.
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