Sentiment analysis
Sentiment analysis uses AI to detect the emotional tone of a message, such as positive, negative, or neutral, from the customer's words.
Sentiment analysis is the use of AI to read the emotional tone behind a piece of text or speech. It classifies a message as positive, negative, or neutral, and can often detect more specific feelings such as frustration, satisfaction, or urgency.
It works by analyzing word choice, phrasing, and context. Modern approaches use language models to pick up on nuance that simple keyword matching would miss, such as sarcasm or politely worded complaints. The result is a signal that systems and people can act on.
In context
In customer support, sentiment analysis helps teams understand how customers feel, not just what they are asking. A message flagged as highly negative or urgent can be prioritized, routed to a senior agent, or escalated before a small issue becomes a churn risk.
Beyond single conversations, sentiment trends across many interactions reveal where customers are consistently frustrated, which product areas cause friction, and whether tone is improving over time. This turns everyday support conversations into a steady source of feedback for the wider business.
How Auralis uses Sentiment analysis
Auralis reads customer tone in real time so Autopilot can prioritize and escalate frustrated or urgent cases, while Audit surfaces sentiment trends across conversations to show where customers feel friction.