Words as Warning Signs: Using Language to Predict Mental Health Struggles

Abstract

The study investigates the use of machine learning models to identify anxiety and depression sentiment in text data, highlighting the decision tree model as the most effective. Logistic regression, AdaBoost, and random forest showed good performance, however, KNN had the worst results. The study highlights the potential of Natural Language Processing (NLP) in detecting mental health disorders early, emphasizing the need to address class imbalance and ethical considerations while selecting models. Future research should focus on studying a variety of demographics and utilizing real-time data collection to improve the generalizability of findings. The research explores how NLP might be used to detect mental health difficulties early by analyzing textual data to uncover patterns related to mental health conditions. Ethical concerns and constraints in applying NLP in mental health are discussed, along with an examination of research demonstrating NLP's efficacy in forecasting mental health disorders. Emphasizing the importance of responsible deployment and execution of NLP in mental healthcare is vital.