Determining intensity of mental state of an unsound individual through text using ML

Abstract

This research investigates the application of machine learning to detect and classify the intensity of various mental health conditions through text analysis. By analyzing user-generated statements, the study aims to identify patterns that correspond to different mental health states, such as Anxiety, Depression, Bipolar Disorder, and Suicidal tendencies. Through rigorous text preprocessing and feature extraction methods, meaningful insights are drawn from the data. The performance of the proposed approach is evaluated through standard metrics, demonstrating its potential to support mental health professionals by automating the initial stages of mental health screening. The findings highlight key challenges, such as language complexity and emotional context, and offer directions for future work to enhance the system’s accuracy and adaptability. This research provides a foundation for developing scalable, automated tools that could be integrated into mental health care and online support platforms.