Personalized Learning Resource Recommendation Text Emotion Recognition Method Based on Deep Transfer Learning

Abstract

In the process of personalized learning resource recommendation, recommendation systems usually combine text data related to the resources themselves with text data related to learners. They analyze learners’ learning needs, interests, and preferences through algorithms. Then they select learning resources that meet learners’ needs from the learning resource library for recommendation. In order to achieve accurate and effective recognition of text emotions in personalized learning resource recommendations, a text emotion recognition method based on deep transfer learning is proposed. Based on the control value theory and the emotional attribute induction method, we will construct a recommended text emotional attribute index system which includes the text emotional attributes type level. For example, we collect multiple text data containing all emotional attributes. Then, we reconstruct the text data set through data cleaning, text analysis, and stop-word removal operations. Furthermore, we extract deep text features based on convolutional neural networks (CNNs). Finally, we integrate deep transfer learning methods to achieve sentiment classification and recognition of recommended text. The experimental results show that the recognition rates of positive and negative emotions in the source target domain text obtained by the design method are 93.5% and 98.2%, respectively; 98.9% and 96.2%, respectively. The mean square error of obtaining emotion recognition results is less than 0.1. This indicates that the knowledge learned from the source data in the design method can be well applied to the target data of personalized learning resource recommendation text. Therefore, it can effectively improve the generalization ability of low-resource datasets. Moreover, it can make reasonable emotional judgments on personalized learning resource recommendation text.