Development of Emotion Stress Relief Recommender Tool Using Machine Learning

In today's society, individuals face unprecedented levels of stress, with varying indicators and effects. Music has long been recognized for its calming effects on the brain and body, especially slow and calm classical music, which can lower heart rate, blood pressure, and stress hormone levels. However, the effectiveness of music in stress management may be reduced if the music does not match the listener's current emotional state. To overcome this problem, this research aims to develop an emotion-based stress reduction recommendation tool using machine learning. This research employs experimental methods. The research results show this tool will analyze the user's emotions and recommend music most likely to reduce stress. By tailoring music recommendations to an individual's emotional state, we hope to increase the overall effectiveness of music as a stress management tool. Implications of this research include the potential for creating intelligent music players that provide more personalized and effective stress reduction.