Utilizing Emotion Analysis for Suicide Prediction and Mental Health Detection in Students with Deep Learning

Abstract

The mental well-being of students is a critical aspect of their overall development and academic success. Emotion analysis and mental health detection play vital roles in identifying students who may be struggling with various psychological challenges. These challenges can range from everyday stressors to more serious mental health disorders. Traditional methods of assessment often rely on self-reporting or observations by professionals, which may not always be accurate or timely. Therefore, leveraging advanced technologies like deep learning can provide more effective and scalable solutions to address these issues. This research paper explores the application of deep learning techniques, particularly CNN, alongside other methodologies, for emotion analysis and mental health detection in students. Deep learning algorithms have demonstrated remarkable capabilities in processing and understanding complex data, making them well-suited for analysing multimodal inputs such as text, audio, and visual cues, which are often present in students' interactions and expressions. By integrating deep learning methods with psychological theories and principles, this study aims to enhance the accuracy and interpretability of emotion analysis and mental health detection models. Specifically, CNNs are employed to learn hierarchical features from the diverse input data, enabling more nuanced interpretations of students' emotional states and mental well-being. The findings of this research are expected to contribute significantly to the development of intelligent systems capable of providing timely and personalized support to students, thereby fostering their mental well-being and academic success.