Stress Analysis in Social Media using ML Algorithms

As the world is advancing towards building a completely digitalized environment with the lifestyle of every individual being immensely affected, it is facile for each to manifest their perspectives and actions on social media platforms. However, such kind of conduct affects the mental health of a person as virtual socialization hinders authentic individualization. Feelings of inadequacy, resentment, and seclusion cynically affect the mind and exacerbate the symptoms of depression, anxiety, and stress. A profound knowledge for ascertaining stress caused due to social media is imperative to avoid unnecessary repercussions. In this proposed method, supervised machine learning algorithms are applied on a dataset of lengthy multi-domain social media data for identifying stress from five different categories of Reddit communities by analyzing the posts when it does not explicitly contain specific keywords such as “stress” or “tension”. Two textual-based featuring methods such as BERT and TF-IDF are used along with machine learning classifiers to adjudge the sentiment of the social media post and categorize them into ‘stress' and ‘non-stress. Exploratory results exemplify that knowledge-enabled BERT is an exceptional solution for sentiment analysis research. The Random Forest classifier achieved the highest accuracy of 75.80% with the BERT fine-tuned model. Furthermore, distinct evaluation metrics, namely, precision, recall, specificity, and F1-score of the models are estimated to single out the ultimate model.