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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Clinical Psychology

Background:

  • Schizophrenia is a severe mental disorder characterized by significant language dysfunction.
  • Emotional states can influence language expression, potentially masking disease-specific features.
  • Previous research indicates differing language patterns between individuals with schizophrenia and healthy controls.

Purpose of the Study:

  • To analyze stable language expression differences in schizophrenia using natural language processing.
  • To investigate the impact of emotional stimuli on language patterns in schizophrenia.
  • To explore the potential of language features in diagnosing schizophrenia and predicting symptom severity.

Main Methods:

  • Recruited 104 schizophrenia patients and 80 healthy controls.
  • Administered emotional stimuli (videos) followed by standardized questions.
  • Employed multivariate analysis of variance, multivariate linear regression, and machine learning techniques.

Main Results:

  • Observed stable, significant differences in conjunction usage and sentence length variability between groups.
  • Identified specific language features (e.g., "Jiushi" frequency, word count, dependency distance) associated with negative symptom severity.
  • Machine learning models achieved 66.3% diagnostic accuracy.

Conclusions:

  • Emotional states influence language in schizophrenia, but stable linguistic markers persist.
  • Conjunction frequency and sentence length variability are reliable indicators of schizophrenia-related language differences.
  • Language features can serve as stable predictors for negative symptoms in schizophrenia.