Sentiment analysis is a method that uses language and acoustic cues to estimate the emotional tone of a customer or agent during a conversation, often summarized as positive, neutral, or negative and tracked over time within the call.
Operationally, it matters because it turns “how the call felt” into a measurable signal you can trend by queue, issue type, agent, or time of day. This helps identify friction points, escalation risk, and coaching opportunities, and it can be paired with other signals (like hold time, transfers, or policy mentions) to understand what is driving negative moments.
Sentiment scores are not the same as customer satisfaction and can be affected by context, sarcasm, accents, and domain language, so they work best as directional indicators and for comparing patterns across many calls rather than judging a single interaction in isolation.
