Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology

Music therapy, enriched by the integration of AI technology, represents a cutting-edge approach to harnessing the therapeutic power of music for mental and emotional well-being. AI algorithms are employed to analyze individual preferences, emotional states, and physiological responses, enabling the creation of personalized music interventions. These interventions can range from mood-enhancing playlists to dynamically generated compositions tailored to the specific needs of the listener. This paper introduces an Optimized Sentimental n-gram Classifier (OSC) model tailored for application in the context of music therapy. Leveraging artificial intelligence (AI) technology and sentiment analysis techniques, the OSC model aims to enhance the understanding and classification of sentiments expressed during music therapy sessions. The OSC model uses the n-gram classifier for the estimation of the feature vector in the music speech signal. The classifier model comprises of the Artificial Intelligence (AI) for the evaluation of the music therapy for the sentimental analysis. Through extensive experimentation and evaluation, the OSC model demonstrates high accuracy, precision, recall, and F1 scores across multiple iterations, indicating its effectiveness in accurately predicting sentiments and classifying sessions. The model's robust performance suggests its potential to assist therapists in better understanding participants' emotional states and tailoring interventions accordingly. By providing a valuable tool for sentiment analysis in music therapy, the OSC model contributes to advancing the integration of AI technology into healthcare practices, with implications for improving patient outcomes and well-being.