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Language sentiment predicts changes in depressive symptoms.

Jihyun K Hur1, Joseph Heffner1, Gloria W Feng1

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Summary
This summary is machine-generated.

Analyzing language sentiment in written responses can help predict future depression symptoms. AI tools, like Large Language Models (LLMs), show promise in identifying individuals at risk for worsening depression.

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Natural Language Processing

Background:

  • Depression is a significant public health issue requiring predictive tools.
  • Previous studies indicate depression affects language use, but predictive power is unclear.

Purpose of the Study:

  • To determine if language sentiment in brief written responses predicts changes in depression.
  • To compare the predictive accuracy of human raters, LLMs, and LIWC for depression symptom changes.

Main Methods:

  • Two studies involving 467 participants who provided written responses and completed depression assessments (PHQ-9).
  • Sentiment analysis of written responses by human raters (N=470), ChatGPT 3.5/4.0, and LIWC.
  • Mood dynamics quantified via a risky decision-making task and momentary happiness measurements.

Main Results:

  • Language sentiment, assessed by human raters and LLMs, predicted a three-week increase in depressive symptoms.
  • LIWC sentiment analysis did not predict symptom changes.
  • Language sentiment correlated with current mood but independently predicted future symptom changes.

Conclusions:

  • Sentiment analysis of brief written responses using AI tools is a scalable method for predicting future depression.
  • AI-powered sentiment analysis matches human performance in predicting psychiatric symptom changes.
  • This approach offers a novel tool for early identification and intervention in depression.