An AI-Enabled Framework for Mood Analysis and Mental Health Support Using User Interaction Data

An AI-Enabled Framework for Mood Analysis and Mental Health Support Using User Interaction Data

Archana Bhat1, Sushree Souravi kar2, Harshitha G M3, Ranjitha S4, Rimah Manal5

1Assistant Professor, Department of Computer Science and Engineering, City Engineering College, Bengaluru-560061, India

2Assistant Professor, Department of CSE (IOT & Cyber Security including Blockchain Technology), City Engineering College

3,4,5 UG Students, Department of Computer Science and Engineering, City Engineering College, Bengaluru-560061, India

Abstract - The prevalence of mental health challenges such as anxiety, depression, and stress is steadily increasing, yet traditional diagnostic methods often remain limited in terms of accessibility, personalization, and the ability to provide real-time insights. To address these gaps, an AI-driven mental health diagnostic platform, has been developed to offer a more effective, data-centric solution. By leveraging advanced machine learning algorithms and behavioral analytics, this platform assesses users’ mental well-being with precision and responsiveness. The platform integrates mood pattern analysis, user interaction monitoring, and self-reported data to deliver personalized diagnostics and tailored mental health recommendations. Through actionable insights, coping strategies, and curated mental health resources, it empowers users to better understand and manage their mental health proactively. Designed for accessibility and ease of use, this framework represents a significant step toward redefining mental health care with a user-centric, real-time, and technology-enabled approach.

Key Words:  Mental Health Diagnostics, Artificial Intelligence, Behavioral Analytics, Mood Pattern Analysis, Personalized Recommendations,