Innovative Approaches to Depression Detection in Student Texts: A BERT-DNN Method

A prevalent mental health illness called depression is characterized by persistent feelings of hopelessness, despair, and disinterest in routine tasks.Existing techniques for depression diagnosis, including conventional surveys and questionnaires, frequently exhibit shortcomings, such as subjective biases and inadequate scalability.The proposed research utilizes Natural Language Processing (NLP) tools to examine textual data, offering a more objective and scalable method for diagnosing depression symptoms.This study seeks to improve the accuracy of depression identification in student-generated text by utilizing a hybrid model that integrates pre-trained BERT (Bidirectional Encoder Representations from Transformers) with a Deep Neural Network (DNN).The process entails obtaining contextual embeddings from BERT, subsequently fine-tuning and classifying using the DNN architecture.The model is assessed using essential performance measures, exhibiting a superior overall accuracy of 97%.A comprehensive performance comparison with existing methods, demonstrates that the novel BERT-DNN model surpasses traditional approaches.The model exhibits enhanced accuracy, precision, recall, and F1-score for both depressed and nondepressed classifications.These findings emphasize the effectiveness of utilizing sophisticated NLP techniques in mental health detection, showcasing the potential for enhanced diagnostic tools that may be included in educational environments and beyond.This study enhances the existing literature on automated depression diagnosis and facilitates future research aimed at creating more sophisticated approaches for mental health assessment via textual analysis.