Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.

Natalie Shapira*, Gal Lazarus, Yoav Goldberg, Eva Gilboa-Schechtman, Rivka Tuval-Mashiach, Daniel Juravski, Dana Atzil-Slonim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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 f2 effect sizes = 0.011 < f2 < 0.022). At the between-client level, only first session use of positive emotion words was associated with first session distress (ηp2 effect size = 0.08). A drop in the use of first-person singular words was associated with improved outcome from pre- to posttreatment (small ηp2 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. (PsycInfo Database Record (c) 2021 APA, all rights reserved) Public Significance Statement—The current study explored whether utterances within sessions contain information about a client's progress. We used automated text analytic techniques to examine whether clients’ linguistic features were associated with their experience of distress both relative to themselves and relative to others, and whether changes in these linguistic features were associated with treatment outcomes. The results indicated that when clients experienced less of distress in the days prior to a session they tended to use fewer first-person singular words, more positive emotion words and fewer negative emotion words in that session. In addition, when clients used fewer negative emotion words in a session, their distress in the days prior to the next session tended to be lower. Lastly, lesser use of first-person singular words in the later versus earlier stages of therapy was associated with improved treatment outcomes. These findings provide preliminary support for the potential use of raw linguistic data within psychotherapy session as a complement to standard monitoring systems to evaluate clients’ progress.

Original languageAmerican English
Pages (from-to)77-87
Number of pages11
JournalJournal of Counseling Psychology
Volume68
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 American Psychological Association

Keywords

  • computerized linguistics measures
  • depression
  • natural language processing
  • outcome measures
  • text analysis

Fingerprint

Dive into the research topics of 'Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.'. Together they form a unique fingerprint.

Cite this