Enhancing Depression Detection Using Pre-trained Multi-modal Sentiment Analysis Models with Deep Prefix Tuning

Depression, a pervasive mental health condition, affects millions globally, challenging early and accurate diagnosis due to its subtle and varied manifestations. Recognizing the critical link between emotional dysregulation and depressive symptoms, our research introduces a pioneering training paradigm that integrates sentiment analysis with depression detection. This approach is motivated by the potential of sentiment data to enrich models with a deeper understanding of emotional states, crucial for identifying depressive patterns. To leverage the nuanced sentiment information without compromising the pre-trained model’s integrity, we employ deep prefix tuning. This novel technique allows for targeted model refinement, ensuring that the valuable pre-trained structures are not overshadowed by the sparse and specific nature of depression-related data. The empirical results demonstrate superior performance across standard benchmarks, setting a new precedent for multimodal depression detection.