Sentiment analysis detects the emotional tone and attitude expressed in speech, identifying whether callers are satisfied, frustrated, angry, or confused. This insight enables appropriate responses and triggers for escalation or special handling.
How does sentiment analysis work?
The system analyzes word choice, phrasing patterns, and sometimes acoustic features like tone of voice and speaking rate. Modern approaches use language models that understand sentiment from context rather than simple keyword matching. Sentiment may be classified categorically (positive/negative/neutral) or on a continuous scale.
Why does sentiment analysis matter?
Caller emotion should influence how the AI responds. A frustrated caller needs acknowledgment and efficiency. An angry caller may need immediate escalation. Detecting sentiment enables adaptive behavior that addresses not just what callers say but how they feel about it.
Sentiment analysis in practice
A caller says “This is the third time I’ve called about this issue.” Sentiment analysis detects frustration. The AI adjusts its approach: “I’m sorry you’ve had to call multiple times about this. Let me look at your history and make sure we resolve this completely today.” The acknowledgment and commitment address the emotional context.