Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis

Background Depression affects people’s daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language processing has emerged as a feasible approach for early mental health screening. However, most existing reviews often included studies with weak depression labels, which affected the reliability of the results and further limited the practical application of the automatic depression estimation models. Objective This review aimed to evaluate the predictive performance of text-based depression models that used standard labels, and to identify text resources, text representation, model architecture, annotation source, and reporting quality contributing to performance heterogeneity. Methods Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched 4 main databases (PubMed, Scopus, IEEE Xplore, and Web of Science) for studies published between 2014 and 2025. The eligible studies were included: machine learning models were developed based on the text generated by the participants and used validated scales or clinical diagnoses as depression labels. Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential moderators. Results We scanned 3067 articles and finally filtered 15 models from 11 studies for the meta-analysis. The overall pooled effect size was 0.605 (95% CI 0.498-0.693), indicating a large strength of association. Subgroup analyses showed that models using embedding-based text representations achieved higher performance than those using traditional features (r=0.741, 95% CI 0.648-0.812 vs r=0.514, 95% CI 0.385-0.623; P