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

Area of Science:

• Psychology
• Linguistics
• Computational Social Science

Background:

• Psychotherapy generates rich linguistic data relevant to client well-being.
• Computerized text analysis offers objective methods to study this data.
• Understanding linguistic markers can enhance treatment monitoring.

Purpose of the Study:

• To investigate the association between linguistic features and client distress levels within psychotherapy.
• To determine if changes in linguistic features correlate with treatment outcome.
• To explore the utility of linguistic analysis in tracking therapeutic progress.

Main Methods:

• Analysis of 729 psychotherapy session transcripts from 58 clients.
• Extraction of linguistic features: first-person singular pronouns, positive and negative emotion words.
• Multilevel models used to examine associations with client-reported distress and treatment outcomes.

Main Results:

• Fewer first-person singular words and more positive emotion words linked to lower distress.
• Reduced use of negative emotion words predicted lower distress in subsequent sessions.
• A decrease in first-person singular word usage correlated with improved treatment outcomes.

Conclusions:

• Linguistic features in psychotherapy sessions show preliminary associations with client distress.
• Computerized linguistic measures hold potential for monitoring therapeutic progress and outcomes.
• This approach may offer objective insights into the dynamics of psychotherapy.