Using Computerized Text Analysis To Examine Associations Between Linguistic Features And Clients' Distress During Psychotherapy

Raw linguistic data within psychotherapy sessions may provide important information about clients' progress and well-being. In the current study, computerized text analytic techniques were applied to examine whether linguistic features were associated with clients' experiences of distress within and between clients and whether changes in linguistic features were associated with changes in treatment outcome. Transcripts of 729 psychotherapy sessions from 58 clients treated by 52 therapists were analyzed. Prior to each session, clients reported their distress level. Linguistic features were extracted automatically by using natural language parser for first-person singular identification and using positive and negative emotion words lexicon. The association between linguistic features and levels of distress was examined using multilevel models. At the within-client level, fewer first-person singular words, fewer negative emotional words and more positive emotional words were associated with lower distress in the same session; and fewer negative emotion words were associated with lower next session distress (rather small f(2) effect sizes = 0.011 < f(2) < 0.022). At the between-client level, only first session use of positive emotion words was associated with first session distress (eta(2)(p) effect size = 0.08). A drop in the use of first-person singular words was associated with improved outcome from pre- to posttreatment (small eta(2)(p) effect size = 0.05). The findings provide preliminary support for the association between clients' linguistic features and their fluctuating experience of distress. They point to the potential value of computerized linguistic measures to track therapeutic outcomes.