AI-DRIVEN SENTIMENTAL ANALYSIS FOR PREDICTING MENTAL HEALTH DISORDER
Abstract
ABSTRACT
This research project aims to develop and evaluate an innovative AI-driven sentimental analysis system designed to predict mental health disorders by analyzing user-generated content on social media and other digital platforms. Mental health issues have become a significant concern globally, affecting millions of individuals and impacting their quality of life, relationships, and productivity. Early detection and intervention are crucial in managing mental health issues, but traditional methods often rely on self-reporting, clinical evaluations, and subjective assessments.
By leveraging natural language processing (NLP) and machine learning (ML) techniques, this project seeks to identify emotional patterns and sentiment shifts indicative of mental health issues, such as depression, anxiety, and stress-related disorders. The proposed system will be trained and evaluated on a large dataset of user-generated content, and its performance will be assessed using metrics such as accuracy, precision, recall, and F1-score.
Key Objectives
1. Develop an AI-driven sentimental analysis framework for mental health disorder prediction.
2. Investigate the effectiveness of NLP and ML techniques in detecting mental health issues.
3. Evaluate the system's performance and identify areas for improvement.
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