Innovative Machine Learning Techniques for Robust Emotion Detection in User Experience and Mental Health Monitoring

Abstract

Emotion detection, an integral component of enhancing user experiences, mental health monitoring, and human-computer interaction, leverages the capabilities of machine learning algorithms to identify and interpret human emotions from diverse data sources such as text, speech, and facial expressions. This study presents a novel emotion detection system, highlighting its effectiveness and reliability. Our proposed method achieves an impressive accuracy of 97.6%, demonstrating its robustness in real-time emotional state analysis. Additionally, the system's performance is validated with a mean absolute error (MAE) of 0.403 and a root mean square error (RMSE) of 0.203, underscoring its precision and consistency. This research not only provides a comprehensive review of existing emotion detection systems but also addresses key challenges and proposes innovative solutions to enhance system accuracy and reliability. By advancing the integration of machine learning in emotion detection, this work aims to contribute significantly to the development of more sophisticated and empathetic human-computer interactions.