Construction of Multi-Label Personality Trait Recognition Model in Chinese Text Based on Emotional Features

Abstract

The key to improve the service effect of virtual digital human is the personality trait recognition technology of users. However, the complexity of Chinese and the sparsity of short texts limit the accuracy of personality trait recognition. Therefore, we applied emotional features to the research of personality trait recognition and constructed a deep learning model for Chinese corpus personality trait recognition to improve the accuracy of personality trait recognition. The model ([Formula: see text] extracted semantic features and embedded emotional features through RoBERTa-wwm-ext Chinese pre-training model, BiLSTM neural network, and attention mechanism. Some experiments were carried out using two Chinese datasets with different scenes. The comparative experiments and ablation experiments showed that the overall effect of the personality trait recognition model ([Formula: see text] is the best, and the Macro-F1 value reaches 75.3%, which is significantly better than the existing models. The results showed that emotional features can significantly improve the accuracy of multi-label personality trait classification in both Chinese daily life scenarios and online scenarios. The proposed model can integrate emotional features and Chinese semantic features to improve the accuracy of personality traits recognition. These findings support the development of personality trait recognition and the application of virtual digital human.