Depression is a global mental health issue generalized by a decrease in mood and satisfaction. Treatments for individuals afflicted with depressive symptoms include prescribed medications that require diagnosis to acquire. The purpose of this investigation was to accurately assist psychiatrists in diagnosis procedures to prevent both false positive and false negative conclusions by utilizing machine learning on social media messages. This was done by training a machine learning algorithm which accurately predicted and detected depressive behaviors and communications. As social media messages encompass individual’s general communications among long periods of time with high consistency and frequency, I hypothesized that social media messages could be used as a method to both train an accurate and consistent machine learning model for the detection of depression. Social media dataset messages rely on self-reported diagnoses. Based on F1 accuracy normalization across machine learning HyperTuning, average accuracy indicated 97% [+/-0.5%] among a ~7600 sample dataset. Utilizing generalized sentimental analysis has shown less accurate results (~80%) but needs further research.
Abstract Depression is a global mental health issue generalized by a decrease in mood and satisfaction. Treatments for individuals afflicted with depressive symptoms include prescribed [...]
Utilizing BERT for the Detection of Depression in Social Media Messages 
