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In this chapter, a comprehensive examination of various text processing algorithms for detecting mental stress is presented. The examination highlights the importance of textual data in different spheres of teenage youth and how this data can become a valuable tool for an alerting system to assess mental health risk. Readers understand how textual data, collected from applications and third-party sources, can be effectively processed at multiple stages, to predict different levels of risk. The chapter introduces diverse deep learning algorithms and explores their application in detecting mental distress in different scenarios. Equipping the readers with ML libraries, tools, and techniques, the chapter empowers them to efficiently train and predict mental distress, enabling a more data-driven and effective approach toward supporting mental health assessment and intervention.

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AI Models from Human–Computer Interaction Textual Data Recorded to Predict Mental Distress in Youth

  • 章节

  • 发表日期：

    2025年

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  • Sharmistha Chatterjee,
  • Azadeh Dindarian,
  • Usha Rengaraju

摘要

In this chapter, a comprehensive examination of various text processing algorithms for detecting mental stress is presented. The examination highlights the importance of textual data in different spheres of teenage youth and how this data can become a valuable tool for an alerting system to assess mental health risk. Readers understand how textual data, collected from applications and third-party sources, can be effectively processed at multiple stages, to predict different levels of risk. The chapter introduces diverse deep learning algorithms and explores their application in detecting mental distress in different scenarios. Equipping the readers with ML libraries, tools, and techniques, the chapter empowers them to efficiently train and predict mental distress, enabling a more data-driven and effective approach toward supporting mental health assessment and intervention.