Psychological Mental Health Analysis using NLP and Machine Learning

This research explores real-time psychological analysis using pre-store information and Natural Language Processing (NLP). It aims to enhance the precision of psychological evaluation by analyzing language patterns in live conversation, utilizing NLP and machine learning techniques. The study addresses mental health challenges, focusing on stress prediction among university students, workers and introverted individuals. Additionally, it delves into depression identification through version NLP techniques and machine learning algorithms. The comparative analysis evaluates the effectiveness of approaches for discerning depression for discerning depressive states from textual data. The research contributes to understanding live text patterns and has implications for personalized mental health interventions, considering ethical considerations in technological advancements.