# Towards Understanding Emotions for Engaged Mental Health Conversations

arXiv: https://arxiv.org/abs/2406.11135 · HTML: https://mlsanity.com/paper/2406.11135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 23 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression](https://mlsanity.com/paper/2504.18932/) (2025)
- ["Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions](https://mlsanity.com/paper/2506.09354/) (2025)
- [Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning](https://mlsanity.com/paper/2512.11276/) (2025)

## Abstract (arXiv)

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.