Detection of Depression in Social Media Posts using Emotional Intensity Analysis

Tapping into digital footprints on social media, this research focuses on providing new insights into detecting depression through textual analysis. Initially, emotional raw data found in social media posts, aimed particularly at the expressions of anger, fear, joy, and sadness, were collected and analyzed. These emotions, each scored by their intensity, offer a quantifiable view into the users' mental state, serving as possible depression markers. Central to the methodological framework adopted is the binary classification system, which classifies texts into depressive or non-depressive states, well founded by the patterns unearthed from the data. The proposed model rigorously trains Artificial Intelligence/Machine Learing (AI/ML) models to traverse through the complexities of natural language, concentrating on noticing delicate indications that signal depression. The introduced models are tested and measured with accuracy, precision, recall, and F1-score. RoBERTa, DistilBERT, and Electra are the transformer-based models emphasized in this research. Their performance is critically evaluated, with the results denoting particular capabilities in understanding and contextualizing language, which is the key advantage in the early identification of mental health issues. This research stands at the intersection of technology and mental health, revolutionizing mental health monitoring and intervention.