Sentiment analysis

Sentiment analysis is the use of AI to detect the emotional tone of customer communications — classifying messages as positive, negative, neutral, or more granularly (frustrated, confused, satisfied, angry, anxious). In customer service, sentiment analysis enables real-time understanding of how customers feel during interactions.

Applications in customer service include:

Real-time escalation: Detecting rising customer frustration and triggering escalation to a human agent before the situation deteriorates
Routing: Prioritizing interactions from distressed or angry customers for faster handling
Quality assurance: Scoring agent (human or AI) empathy and tone across interactions
Trend analysis: Tracking aggregate customer sentiment over time, by product, by issue type, or following product launches and outages
Voice of customer: Aggregating sentiment across all channels to understand overall customer health

Modern sentiment analysis goes beyond simple positive/negative classification. Nuanced systems can detect:

Sarcasm ("Oh great, another update that breaks everything")
Mixed sentiment ("I love the product but your support is terrible")
Urgency without negative sentiment ("I need this resolved before my trip tomorrow")
Escalating frustration across a conversation (customer started calm but is getting increasingly terse)

For CX teams, sentiment analysis is most valuable as an input to other systems rather than a standalone metric. Sentiment-informed routing, sentiment-triggered escalation, and sentiment-based QA scoring are all more actionable than a sentiment dashboard alone. The goal is to act on emotional signals in real-time, not just report on them after the fact.