Web-Based Platform for Quantitative Depression Risk Prediction via VAD Regression on Korean Text and Multi-Anchor Distance Scoring

Abstract

Depression risk prediction benefits from approaches that go beyond binary labels by offering interpretable, quantitative views of affective states. This study presents a web-based platform that estimates depression risk by combining Korean Valence-Arousal-Dominance (VAD) regression with a structured, multi-anchor distance scoring method. We construct a Korean VAD-labeled resource by integrating the NRC-VAD Lexicon, the AI Hub emotional dialogue corpus, and translated EmoBank entries, and fine-tune a KLUE-RoBERTa regression model to predict sentence-level VAD vectors. Depression risk is then derived as the mean Euclidean distance from the predicted VAD vector to depressive anchor vectors and normalized into an interpretable risk index. In evaluation, the approach shows strong agreement with ground truth (Pearson's r=0.87) and supports accurate risk screening when thresholded. The platform provides intuitive visual feedback for end users and monitoring tools for professionals, highlighting the practicality of integrating interpretable VAD modeling with lightweight scoring in real-world, web-based mental health support.