Towards Understanding Emotions for Engaged Mental Health Conversations

TL;DR

This paper explores passive emotion detection through keystroke dynamics and sentiment analysis in text-based mental health support systems to enhance crisis intervention and care quality.

Contribution

It introduces a novel system combining keystroke and sentiment analysis for emotion sensing in mental health text interactions, advancing AI support tools.

Findings

Keystroke and sentiment analysis can infer emotional states from short texts.

Preliminary results suggest potential for real-time emotion monitoring.

The approach supports mental health providers in crisis care.

Abstract

Providing timely support and intervention is crucial in mental health settings. As the need to engage youth comfortable with texting increases, mental health providers are exploring and adopting text-based media such as chatbots, community-based forums, online therapies with licensed professionals, and helplines operated by trained responders. To support these text-based media for mental health--particularly for crisis care--we are developing a system to perform passive emotion-sensing using a combination of keystroke dynamics and sentiment analysis. Our early studies of this system posit that the analysis of short text messages and keyboard typing patterns can provide emotion information that may be used to support both clients and responders. We use our preliminary findings to discuss the way forward for applying AI to support mental health providers in providing better care.