What is Sentiment Analysis?
Sentiment analysis uses AI and natural language processing (NLP) to detect the emotional tone of customer interactions. By analyzing word choice, speech patterns, and contextual cues, sentiment analysis classifies conversations as positive, negative, or neutral—helping contact centers identify at-risk customers, quality issues, and coaching opportunities.
How Sentiment Analysis Works
- Text analysis — Evaluates word choice, punctuation, emojis
- Voice analysis — Detects tone, pitch, speaking pace, volume
- Contextual understanding — Considers conversation flow and history
- Real-time scoring — Updates sentiment throughout the interaction
Use Cases for Sentiment Analysis
- Quality management — Flag calls with negative sentiment for review
- Real-time alerts — Notify supervisors when calls go negative
- Trend analysis — Track sentiment changes over time
- Churn prediction — Identify customers at risk of leaving
- Agent coaching — Find coaching opportunities in negative calls
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