Prediction Method of Human Group Emotion Perception Tendency Based on a Machine Learning Model

Abstract

Humans with the same attribute tend to assume similar emotional and cognitive tendencies. In this study, a machine learning model was constructed to calculate the sample emotion categories’ membership matrices of human groups towards a group of aria samples, thus making a prediction of the human groups’ emotion perception tendencies. Subjects’ binary attributes in terms of gender, family environment and professional background were collected. The subjects were invited to perform an emotion identification of aria segments to build a multi-classification standard dataset of differences in subjects’ emotion identification. A dual-channel neural network model was proposed, and three neural networks based on it were adopted to predict the emotion perception tendencies of the categorical groups with three attributes. Subjects were clustered according to the predicted values of the emotion perception tendencies towards the sample test group, followed by a comparison of the clustering results with the actual attribute group classifications. As suggested by the experimental results, the neural network accurately predicted the classification groups’ emotion perception tendencies with the three attributes of the subjects. The cluster experiment results of subjects, based on the predicted values of the neural networks’ output, had better performance only under the professional background attribute.