Chinese Text Sentiment Analysis Based on BI-GRU and Self-attention

Abstract

Humans have been trying to get machines to understand human emotional states, and using natural language processing techniques for sentiment analysis is the mainstream solution. In order to solve the problem of high complexity and low efficiency in Chinese LSTM-based Chinese text sentiment analysis, this paper uses Bi-GRU (bidirectional GRU neural network) and attention mechanism model to analyze Chinese text sentiment. First of all, the model can extract deep features of the text, and combine the context of the sentence to learn the text features more accurately. Secondly, the model introduces Multi-Head Self-Attention, which reduces the dependence on external parameters, assigns weights to word vectors, highlights text features, and pays more attention to the internal dependencies on sentences. Experiments show that the Chinese text sentiment analysis based on the model can get 87.1% accuracy.