  In disentangling the heterogeneity observed in psychopathology, personality
of the patients is considered crucial. While it has been demonstrated that
personality traits are reflected in the language used by a patient, we
hypothesize that this enables automatic inference of the personality type
directly from speech utterances, potentially more accurately than through a
traditional questionnaire-based approach explicitly designed for personality
classification. To validate this hypothesis, we adopt natural language
processing (NLP) and standard machine learning tools for classification. We
test this on a dataset of recorded clinical diagnostic interviews (CDI) on a
sample of 79 patients diagnosed with major depressive disorder (MDD) -- a
condition for which differentiated treatment based on personality styles has
been advocated -- and classified into anaclitic and introjective personality
styles. We start by analyzing the interviews to see which linguistic features
are associated with each style, in order to gain a better understanding of the
styles. Then, we develop automatic classifiers based on (a) standardized
questionnaire responses; (b) basic text features, i.e., TF-IDF scores of words
and word sequences; (c) more advanced text features, using LIWC (linguistic
inquiry and word count) and context-aware features using BERT (bidirectional
encoder representations from transformers); (d) audio features. We find that
automated classification with language-derived features (i.e., based on LIWC)
significantly outperforms questionnaire-based classification models.
Furthermore, the best performance is achieved by combining LIWC with the
questionnaire features. This suggests that more work should be put into
developing linguistically based automated techniques for characterizing
personality, however questionnaires still to some extent complement such
methods.
