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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Assessing Heterogeneity in Sentiment Changes in Text-Based Counseling: Latent Class Trajectory Analysis.

Ziru Fu1, Yu Cheng Hsu2, Christian Shaunlyn Chan3

  • 1Department of Social Work and Social Administration, Faculty of Social Sciences, Faculty of Social Sciences, University of Hong Kong, Hong Kong, China (Hong Kong).

Journal of Medical Internet Research
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

Researchers identified three sentiment trajectories in online counseling: steady improvement, deterioration, and dip-then-rebound. Early identification of these patterns can enhance text-based mental health support.

Keywords:
growth mixture modellatent class trajectory analysismultinomial logistic regressionsentiment analysistext-based counseling

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Area of Science:

  • Digital Mental Health
  • Computational Social Science
  • Clinical Psychology

Background:

  • Online text-based counseling is growing, but its nature presents challenges in monitoring user emotional shifts.
  • Anonymity and text format hinder personalized interventions, potentially impacting service effectiveness and user satisfaction.

Purpose of the Study:

  • To identify distinct within-session sentiment trajectories of help-seekers in online text-based counseling.
  • To examine key variables associated with different sentiment trajectory memberships.

Main Methods:

  • Latent class trajectory analysis using a growth mixture model (GMM) on 6207 counseling sessions.
  • Sentiment scores of help-seeker messages, labeled by ChatGPT, were used for trajectory modeling.
  • Multinomial logistic regression identified variables associated with class membership.

Main Results:

  • Three distinct sentiment trajectories were identified: steady improvement (18.9%), deterioration (18.0%), and dip-then-rebound (63.1%).
  • The deterioration trajectory was significantly associated with suicidal ideation, family/physical health concerns, and premature session departure.

Conclusions:

  • Distinct within-session sentiment trajectories exist in online text-based counseling.
  • Early identification of these trajectories can enable counselors to adapt interventions, improving effectiveness and satisfaction in digital mental health services.