Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 28, 2025
Date Accepted: Jul 30, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A trajectory-based analysis of heterogeneity in sentiment changes among help-seekers in text-based counselling
ABSTRACT
Background:
Online text-based counselling services are becoming increasingly popular. However, their text-based nature and anonymity pose challenges in tracking and understanding shifts in help-seekers’ emotional experience within a session. These characteristics make it difficult for service providers to tailor interventions to individual needs, potentially diminishing service effectiveness and user satisfaction.
Objective:
This study sought to identify distinct within-session sentiment trajectories among help-seekers in online text-based counselling and examine key predictors of trajectory membership.
Methods:
A total of 6,207 counselling sessions were randomly extracted from an online text-based counselling service in Hong Kong. A Latent Class Trajectory Analysis (LCTA) of help-seekers’ in-session sentiment was conducted using a Growth Mixture Model (GMM) to identify latent groups of help-seekers exhibiting specific sentiment trajectories. Sentiment scores of help-seeker messages, labelled by ChatGPT, served as the primary variable for trajectory modelling. Subsequently, a multinomial logistic regression was performed to identify variables associated with class membership.
Results:
The GMM identified three distinct sentiment trajectories as the best fit: i) Steady Improvement (18.9%), ii) Deterioration (18.0%), and iii) Dip-Then-Rebound (63.1%). Compared to the Dip-Then-Rebound Class, help-seekers in the Deterioration Class were more likely to report suicidal ideation, present with family or physical health-related concerns, have an unknown gender status, access the service through the anonymous channel, depart from the session prematurely, and have shorter session durations.
Conclusions:
We identified three distinct trajectories of help-seekers’ in-session sentiment. Identifying the most likely trajectory at an early stage in the session could potentially help counsellors adjust their approaches, thereby improving the effectiveness of text-based counselling and enhancing help-seeker satisfaction.
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