StudentEmoScan: Applying Machine Learning, Problem-Solving, and Decision-Making Algorithms to Analyze Emotional Patterns and Detect Mental Health Issues in Students

Abstract

Mental diseases can have a major impact on people's cognitive processes and behaviours, emphasising the significance of early discovery for successful treatments. This article studies how social media monitoring might help discover underlying mental health disorders by analysing expressions, writing styles, and emotional states. The study investigates the effectiveness of machine learning algorithms in Predicting mental health consequences through social media data by examining numerous mental algorithms that regulate information processing and decision-making. Different approaches are compared and evaluated using a comprehensive evaluation of existing mental state detection algorithms, to identify the most promising methods for mental health detection. The study combines insights from methodological comparisons and literature reviews to address current issues and restrictions, as well as propose future research options to overcome these hurdles.