Language dysfunction has long been described in schizophrenia and most studies have focused on characteristics of structure and form. This project focuses on the content of language based on autobiographical narratives of five basic emotions. In persons with schizophrenia and healthy controls, we employed a comprehensive automated analysis of lexical use and we identified specific words and semantically or functionally related words derived from dictionaries that occurred significantly more often in narratives of either group. Patients employed a similar number of words but differed in lower expressivity and complexity, more self-reference and more repetitions. We developed a classification method for predicting subject status and tested its accuracy in a leave-one-subject-out evaluation procedure. We identified a set of 18 features that achieved 65.7% accuracy in predicting clinical status based on single emotion narratives, and 74.4% accuracy based on all five narratives. Subject clinical status could be determined automatically more accurately based on narratives related to anger or happiness experiences and there were a larger number of lexical differences between the two groups for these emotions compared to other emotions.
Keywords: Diction; Emotion; LIWC; Learning-based analyses; Lexical features; Text classification.
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