Research on Chinese Emotion Classification using BERT-RCNN-ATT

Abstract

Emotional classification is the process of analyzing and reasoning subjective texts with emotional color, that is, analyzing whether their emotional tendencies are positive or negative. Aiming at the problems of massive data and nonstandard words in the existing Chinese short text emotion classification algorithm, the traditional BERT model does not distinguish the semantics of words with the same sentence pattern clearly, the multi-level transformer training is slow, time-consuming, and requires high energy consumption, this paper proposes to classify users' emotions based on BERT-RCNN-ATT model, and extract text features in depth using RCNN combined with attention mechanism, Multi task learning is used to improve the accuracy and generalization ability of model classification. The experimental results show that the proposed model can more accurately understand and convey semantic information than the traditional model. The test results show that compared with the traditional CNN, LSTM, GRU models, the accuracy of text emotion recognition is improved by at least 4.558%, the recall rate is increased by more than 5.69%, and the F1 value is increased by more than 5.324%, which is conducive to the sustainable development of emotion intelligence combining Chinese emotion classification with AI technology.