ERNIE-BiLSTM Based Chinese Text Sentiment Classification Method

Abstract

For the Chinese text sentiment classification task, the preprocessing based on deep learning models cannot retain the information and polysemy of the word in the sentence well. So this paper adopts the newly developed ERNIE [1–2] (Knowledge Enhanced Semantic Representation) pre-training model from Baidu, which is based on word feature input modeling, not only enhances the semantic representation of the word, but also preserves the contextual information of the word and the polysemy of the word. After pre-training by ERNIE model, the output word vector is used as the input of BiLSTM (bidirectional long and short-term memory network) model for training and obtaining sentiment classification results. The accuracy rate of Ernie bilstm model is 92.35% after verification on nlpcc2014 microblog sentiment analysis sample data set, which proves that the model has good performance in Chinese text sentiment classification task.