EmoShown: AI-Powered Emotional Wellness Hub with Sentiment Analysis, Anomaly Detection, and Collaborative Filtering

An increasing global concern, mental health issues profoundly affect individuals' well-being and productivity, posing significant societal and economic challenges. This study introduces EmoShown, a mobile application that leverages artificial intelligence to enhance emotional wellness through sentiment analysis, anomaly detection, and collaborative filtering. EmoShown incorporates the VADER algorithm, achieving an accuracy of 85%, a precision of 0.86, and a recall of 0.85 for sentiment analysis. The anomaly detection feature, powered by Isolation Forest, identifies emotional pattern deviations with an accuracy of 92%, a precision of 0.74, and a recall of 1.0. The collaborative filtering system, utilizing matrix factorization, delivers personalized activity recommendations with an accuracy of 81%, and a precision and recall of 0.81. These results highlight the app's effectiveness in providing useful information and personalized support. The app fills the gaps in traditional emotional health tools, offering a comprehensive, data-driven approach to mental wellness. By integrating user mood journals, sentiment interpretation, and preference-based recommendations, EmoShown delivers proactive emotional insights and early detection of mental health concerns. Future work will focus on enhancing AI model performance, ensuring robust data privacy, and expanding features to cater to diverse user needs. EmoShown underscores the potential of AI-powered solutions in addressing global mental health challenges.