Semantic and Emotional Feature Fusion Model for Early Depressive Prediction

In recent years, depression has caused severe social and psychological problems. The purpose of the paper is to automatically identify users with depressive tendencies to facilitate early intervention and prevent the progression of depression into more severe consequences. The paper proposes a Depression Prediction model based on Multi-feature Fusion (DPMFF), which extracts contextual semantic features and deep emotional features from user documents to predict depression risk. The behavioral and linguistic features of depressed users were examined through statistical analysis. Experiments on micro-blog datasets demonstrate that DPMFF can effectively identify users with depressive tendencies and outperform other models. The data analysis found that compared with normal users, users with depressive tendencies were usually active on social networks late at night, and the proportion of content containing absolute words and negative words was significantly higher than average.