Towards Understanding Emotions for Engaged Mental Health Conversations
This work addresses the need for better engagement tools in mental health care, particularly for youth using text-based platforms, though it appears incremental as it builds on existing emotion-sensing techniques.
The paper tackles the problem of providing timely mental health support through text-based media by developing a system that uses keystroke dynamics and sentiment analysis for passive emotion-sensing, with early studies suggesting it can extract emotion information from short messages and typing patterns to aid clients and responders.
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.