Detecting Mental Disorders in Social Media Through Emotional Patterns (KEY IJP************816)

• G. Sumanth Reddy,M. Suhaas ,Sufiyan Ali Khan,Ch. Sumedh Acharya,V. Sudhiksha,Prof. G. Apparao

Abstract

The project was entitled Detecting Mental Disorders in Social Media Through Emotional Patterns Detecting mental disorders through emotional patterns in social media offers an innovative solution to the increasing prevalence of mental health challenges. Traditional diagnostic methods often fail to provide timely detection and support, making it essential to explore accessible alternatives. This research focuses on identifying early signs of conditions such as depression, anxiety, anorexia, and bipolar disorders by analyzing emotional patterns and behavioral trends in social media posts using machine learning (ML) and natural language processing (NLP). Key indicators like grief, anxiety, and mood fluctuations are used to detect potential mental health issues, enabling early intervention and support. The study leverages the power of ML and NLP to analyze large-scale social media data, identifying distress signals and creating a framework for proactive mental health care. This approach not only reduces the stigma around mental health but also provides an accessible and scalable method for early detection, benefiting individuals and society at large. To ensure ethical use of technology, the research strictly adheres to privacy and data security standards. Publicly available data is analyzed responsibly, with stringent guidelines to protect user rights and confidentiality. The potential applications of this framework extend across various online platforms, fostering a supportive digital environment for individuals in need. By integrating technology and mental health care, this research paves the way for innovative, non-intrusive solutions to address mental health issues, offering a meaningful impact on both individual well-being and societal health.