Ensemble Machine Learning Approach for Stress Detection in Social Media Texts

Abstract

Stress is an almost basic human instinct, especially in the internet age. People’s health is being endangered by psychological stress. It is critical to diagnose stress early on to provide proactive therapy. In the age of the Internet, social media has an enormous impact on human thinking. It can cause mental health issues such as stress but can also play an important part in detecting it. Advancements in machine learning and natural language processing have enabled information extraction from a huge amount of raw textual data. In this study machine learning approach is used to detect stress in a social media text. Data used in this study is from Reddit. Classical and Ensemble machine learning approaches were used for stress detection. Classical Techniques include Decision Tree, Logistic Regression, support vector machine, Random Forest, and Na ̈ıve Bayes. Ensemble approaches used are boosting, bagging, and voting. The ensemble approach was able to provide better results than all machine learning baselines on the dataset. Also, it was able to outperform all non-transformed-based neural network architectures discussed in the baseline. The best-performing model in this study is Logistic regression with a 76.6% F1 Score.