Towards Understanding Emotions for Engaged Mental Health Conversations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910490622427136 |
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| author | Sim, Kellie Yu Hui Fortuno, Kohleen Tijing Choo, Kenny Tsu Wei |
| author_facet | Sim, Kellie Yu Hui Fortuno, Kohleen Tijing Choo, Kenny Tsu Wei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_11135 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Understanding Emotions for Engaged Mental Health Conversations Sim, Kellie Yu Hui Fortuno, Kohleen Tijing Choo, Kenny Tsu Wei Human-Computer Interaction Artificial Intelligence H.5.2; I.2.7 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. |
| title | Towards Understanding Emotions for Engaged Mental Health Conversations |
| topic | Human-Computer Interaction Artificial Intelligence H.5.2; I.2.7 |
| url | https://arxiv.org/abs/2406.11135 |