A New Model for Emotion Prediction in Music

Abstract

Music based sentiment analysis has various applications in the form of music recommendation system, sales and advertisement etc. Various studies have dealt with lyrics and used Natural Language Processing to perform sentiment analysis. Others have directed their focus on the audio features to find relevant answers. But the biggest challenge faced while predicting music emotions is that no music depicts only a single emotion. Therefore, in this study, Russell’s scale is used to predict arousal and valence, rather than emotion. Audio feature selection via Multi-linear Regression is performed and comparative study is done between Linear Support Vector Machine, Decision Tree, Kernel SVM, K nearest neighbours (K-NN), Naive Bayes, Logistic Regression and Random Forest on the audio features. Moreover, a hybrid model based on Multi-Layer Perceptron is proposed to enhance the precision of the predictions. The data set of this research has been taken from PMEmo 2019 data.