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

ABSTRACT:

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 It

SUBMITTER: Zhang S 

PROVIDER: S-EPMC12936666 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature