A Multi-Model Approach to Detection of Depression in the Chinese Social Media Entries

To address a significant gap in the field of mental health research, this study presents a multi-model approach to detecting depression in Chinese social media posts: a combination of the N-gram model, Normalization and N-gram Analysis, Emotion Vector Extraction (BERT) model and neural network model. These models not only capture linguistic patterns characteristic of depressive expressions but also extract emotional vectors to identify complex emotional states, enhancing the accuracy and generalizability of depression detection. Furthermore, the study incorporates dropout regularization and cross-validation techniques to mitigate overfitting, ensuring robust model performance. By focusing on the Chinese-speaking demographic and addressing the unique linguistic and cultural dynamics of Chinese social media, this research fills a critical void and paves the way for more inclusive and effective mental health support tools in the digital age, contributing to a globally inclusive understanding of mental health.