frustration-detection

Detect user frustration by observing text signals like ALL CAPS, repetition, latency, and negative affect. Analyze these signals across multiple chat turns to trigger adaptive responses and improve user satisfaction.

Cumulative signal scoring tracks frustration patterns across conversation turns to establish escalation thresholds. When user frustration signals breach these thresholds, the system triggers tone adaptation or initiates a handoff to human support.

Silent adaptation in affective computing adjusts response tone and pace based on detected user frustration without explicitly naming the emotion. This prevents disengagement by gracefully repairing the interaction without alarming the user.

Yes, frustration detection supports escalation design by using cumulative pattern scoring to determine when to hand off a chat interaction to a human. Escalation thresholds ensure handoffs occur only when graceful repair attempts are insufficient.

Text signals for tone calibration include ALL CAPS messages, repeated phrases, response latency, hedges, and negative affect words. Monitoring these indicators allows conversational AI to accurately assess user state and adjust responses accordingly.

No, frustration detection operates without specific dependencies or components. It functions by directly monitoring user messages for frustration signals and applying cumulative scoring to adjust tone or escalate interactions natively.