Lexical use in emotional autobiographical narratives of persons with schizophrenia and healthy controls
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 em...
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| Published in | Psychiatry research Vol. 225; no. 1-2; pp. 40 - 49 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
30.01.2015
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| Abstract | 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.
•We examined the content of language based on autobiographical narratives of experiences evoking five basic emotions.•Automated analysis was performed of lexical use of the narratives of persons with schizophrenia and healthy controls.•Patients showed lower expressivity and language complexity.•Employing features predicted subject status, i.e., persons with schizophrenia and healthy controls. |
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| AbstractList | 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.
•We examined the content of language based on autobiographical narratives of experiences evoking five basic emotions.•Automated analysis was performed of lexical use of the narratives of persons with schizophrenia and healthy controls.•Patients showed lower expressivity and language complexity.•Employing features predicted subject status, i.e., persons with schizophrenia and healthy controls. 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.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. Abstract 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. 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. |
| Author | Hong, Kai Kohler, Christian G. Verma, Ragini Parker, Amber P. Nenkova, Ani March, Mary E. |
| Author_xml | – sequence: 1 givenname: Kai surname: Hong fullname: Hong, Kai email: hongkai1@seas.upenn.edu organization: Department of Computer and Information Science, University of Pennsylvania School of Engineering and Applied Science, USA – sequence: 2 givenname: Ani surname: Nenkova fullname: Nenkova, Ani email: nenkova@seas.upenn.edu organization: Department of Computer and Information Science, University of Pennsylvania School of Engineering and Applied Science, USA – sequence: 3 givenname: Mary E. surname: March fullname: March, Mary E. email: memarch@mail.med.upenn.edu organization: Schizophrenia Research Center, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA – sequence: 4 givenname: Amber P. surname: Parker fullname: Parker, Amber P. email: parker@sas.upenn.edu organization: University of Pennsylvania School of Art and Sciences, Philadelphia, PA 19104, USA – sequence: 5 givenname: Ragini surname: Verma fullname: Verma, Ragini email: ragini.verma@gmail.com organization: Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA – sequence: 6 givenname: Christian G. surname: Kohler fullname: Kohler, Christian G. email: kohler@mail.med.upenn.edu organization: Schizophrenia Research Center, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25480546$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adult Diction Emotion Emotions Humans Learning-based analyses Lexical features LIWC Male Narration Psychiatry Schizophrenia - classification Schizophrenia - diagnosis Schizophrenic Psychology Semantics Text classification Verbal Behavior Vocabulary |
| Title | Lexical use in emotional autobiographical narratives of persons with schizophrenia and healthy controls |
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