Deep Learning-Based Approach to Detect Depressive Tendencies from Social Media Posts by Leveraging Advanced NLP Techniques and Multimodal Data Analysis
TL;DR
The results demonstrate that deep learning-based approaches outperform traditional machine learning methods in detecting depressive expressions from social media posts and suggest that AI can play a crucial role in early depression detection and intervention, contributing to proactive mental health care solutions.
Abstract
Depression is a prevalent mental health disorder that significantly impacts individuals' well-being and daily life. With the increasing use of social media, users often express emotions, thoughts, and behaviours that can indicate mental health conditions, including depression. This study proposes a deep learning-based approach to detect depressive tendencies from social media posts by leveraging advanced Natural Language Processing (NLP) techniques and multimodal data analysis. We utilize transformer-based models such as BERT, RoBERTs, and XLNet for feature extraction and sentiment analysis. Additionally, we explore hybrid deep learning architectures integrating CNNs, LSTMs, and attention mechanisms to enhance contextual understanding. To improve accuracy and generalization, the dataset is augmented using selfsupervised learning techniques and pre-trained embeddings fine-tuned on mental health-related corpora. The model is evaluated on benchmark datasets using precision, recall, F1-score, and AUC-ROC metrics. The results demonstrate that deep learning-based approaches outperform traditional machine learning methods in detecting depressive expressions from social media posts. Furthermore, we discuss the ethical considerations and potential applications of AI-driven depression detection in real-world mental health support systems. Our findings suggest that AI can play a crucial role in early depression detection and intervention, contributing to proactive mental health care solutions.