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Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation.

Lu Ren1, Hongfei Lin1, Bo Xu1,2

  • 1Dalian University of Technology, Dalian, China.

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Summary
This summary is machine-generated.

This study introduces an emotion-based attention network to improve automatic depression detection by capturing high-level emotional semantics. The novel model demonstrates competitive performance, highlighting the effectiveness of emotional semantic information in identifying depression.

Keywords:
algorithmattention networkdeep learningdepression detectiondynamic fusion strategyemotionemotional semantic informationmental healthnatural language processingsocial media

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Artificial intelligence in healthcare

Background:

  • Depression is a prevalent mental health issue with significant global impact, including links to suicide.
  • Current automatic depression detection methods using natural language processing often overlook high-level emotional semantic information.
  • Existing deep learning models struggle to accurately extract effective emotional semantic features for depression detection.

Purpose of the Study:

  • To propose an emotion-based attention network for enhanced depression detection.
  • To effectively capture high-level emotional semantic information crucial for accurate depression identification.

Main Methods:

  • Developed a semantic understanding network to capture contextual semantic information.
  • Introduced an emotion understanding network with positive and negative emotion units.
  • Implemented a dynamic fusion strategy within the emotion understanding network to integrate emotional data.

Main Results:

  • The proposed emotion-based attention network achieved high performance on the Reddit dataset.
  • Achieved an accuracy of 91.30%, precision of 91.91%, recall of 96.15%, and F-measure of 93.98%.
  • Results are comparable to state-of-the-art methods in depression detection.

Conclusions:

  • The developed model is competitive with existing state-of-the-art approaches.
  • The semantic understanding network, emotion understanding network, and dynamic fusion strategy are effective components for depression detection.
  • Emotional semantic information significantly contributes to improved depression detection accuracy.