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Emotion-dependent language featuring depression.

Chaoqing Yang1, Xinying Zhang1, Yuxuan Chen1

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Analyzing speech and word use in different emotional states can improve depression detection. Patients with depression exhibit distinct linguistic patterns, particularly in emotion-dependent narration, aiding in identifying depressive symptoms.

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Area of Science:

  • Psycholinguistics
  • Clinical Psychology
  • Computational Linguistics

Background:

  • Depression is linked to emotional dysfunction and emotion-dependent cognitive processes.
  • Language features in depression are crucial for disorder detection.
  • Understanding emotion-specific word use in depression is key.

Purpose of the Study:

  • To investigate speech features and word use in emotion-dependent narrations of depression patients.
  • To identify linguistic indicators differentiating depressed individuals from controls across various emotions.
  • To explore how emotional context influences language in depression.

Main Methods:

  • Forty patients with depression and forty controls narrated self-relevant memories under five emotional conditions (sad, angry, fearful, neutral, happy).
  • Recorded speech and transcribed texts were analyzed for linguistic features.
  • Linguistic indicators were correlated with depression severity.

Main Results:

  • Depression patients spoke slower and less than controls.
  • Significant differences in word use (negative emotion, work, family, health, etc.) were observed between groups, irrespective of emotion.
  • Emotion-dependent word use patterns (pronouns, tense, causation, death, psychology) further distinguished groups and explained 71.6% of depression severity variance.

Conclusions:

  • Analyzing word use and speech features within different emotional contexts enhances depression detection accuracy.
  • Linguistic analysis of emotion-dependent narration offers a promising avenue for identifying depressive symptoms.
  • Further research with larger datasets is needed to confirm findings.