Detecting the Severity of Depression in Online Forum Data by Leveraging Implicit Semantic

Abstract

Depression, a prevalent mental health disorder with global implications, exerts a profound negative influence on individuals’ lives. While the prediction of depression (as a binary classification task) is a well-established research area, depression severity detection is a new research direction with limited studies. In the context of detecting the severity of depression through online forum data, this research endeavors to offer two distinct solutions by employing Ada embeddings, GPT 3.5 Turbo, and LIWC as feature engineering techniques, while AutoSklearn serves as the ensemble learning algorithm. Notably, the outcomes of this study significantly o utperform e xisting state-of-the-art models on both depression severity annotated datasets used in this research. The results also showcase the potential reuse nature of the proposed models in diverse data sources due to their high performance in both datasets. Furthermore, as a valuable practical outcome, a software prototype has been developed, capable of providing the depression severity level, along with associated symptoms and keywords, upon inputting an online forum post