Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

Abstract

One of the mental threat for individual's health identified is Psychological stress from social media data. Hence, necessity is to predict and manage stress before it turns into a serious problem. However, Conventional stress detection methods exist, that rely on psychological scales & physiological devices that need full of victims participation which is time-consuming, complex and expensive. With the trending growth of social networks, people are addicted towards sharing personal moods via social media platforms to influence other users, leading to stressfulness. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the victims's psychological stress from social media data. It comprises of three (3) modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information; we defined set of stress-related textual 'F = {f1, f2, f3, f4}', visual 'vF = {vf1, vf2}', and social features 'sf' to predict stress from social media content. Experimental results show the proposed PSD model improves the detection process when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model will assist in developing stress detection tools for mental health agencies.