Using Topic Models to Identify Clients’ Functioning Levels and Alliance Ruptures in Psychotherapy

Dana Atzil-Slonim, Daniel Juravski, Eran Bar-Kalifa, Eva Gilboa-Schechtman, Rivka Tuval-Mashiach, Natalie Shapira, Yoav Goldberg

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Computerized natural language processing techniques can analyze psychotherapy sessions as texts, thus generating information about the therapy process and outcome and supporting the scaling-up of psychotherapy research. We used topic modeling to identify topics discussed in psychotherapy sessions and explored (a) which topics best identified clients’ functioning and alliance ruptures and (b) whether changes in these topics were associated with changes in outcome. Transcripts of 873 sessions from 58 clients treated by 52 therapists were analyzed. Before each session, clients self-reported functioning and symptom level. After each session, therapists reported the extent of alliance rupture. Latent Dirichlet allocation was used to extract latent topics from psychotherapy textual data. Then a sparse multinomial logistic regression model was used to predict which topics best identified clients’ functioning levels and the occurrence of alliance ruptures in psychotherapy sessions. Finally, we used multilevel growth models to explore the associations between changes in topics and changes in outcome. Session-based processing yielded a list of semantic topics. The model identified the labels above chance (65% to 75% accuracy). Change trajectories in topics were associated with change trajectories in outcome.

Original languageEnglish
Pages (from-to)324-339
Number of pages16
Issue number2
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 American Psychological Association


  • Machine learning
  • Natural language processing
  • Psychotherapy process and outcome
  • Text
  • Topic models


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