Analysis of Chinese text emotions combining BERT and BiSRU-AT.

To address the problems that the traditional language model cannot solve the problem of word ambiguity in word vector representation and the existing models of emotion classification cannot capture long distance semantic information, the analysis of text emotions combining BERT (Bidirectional Encoder Representation from Transformers) and BiSRU-AT is proposed. Firstly, BERT is used to obtain the word vector representation that integrates text semantics, and then BiSRU (Bidirectional Simple Recurrent Unit) is used to extract context information again. Secondly, the attention mechanism is utilized to assign corresponding weights to the outputs of BiSRU layer to highlight the key information. Finally, softmax regression is used to obtain the sentence level emotional probability distribution. Experiments are carried out on the Twitter dataset and hotel comment dataset. The results show that the analysis of text emotions model combining BERT and BiSRU-AT can achieve higher accuracy, and the BiSRU model and the adoption of attention mechanism can effectively improve the overall performance of the model which is of great pragmatic value. [ABSTRACT FROM AUTHOR]