Sentiment analysis of Chinese text based on Elmo-RNN model

Abstract

Traditional word embedding technologies such as Word2Vec and GloVe can only produce a single semantic vector, and can not get the specific meaning of polysemous words in the text combined with context. To solve this problem, a sentiment analysis model of Chinese text based on Elmo and recurrent neural network is proposed. The model uses Elmo model to learn the pre training corpus, and the BiLSTM network structure in Elmo model makes the word vector generated by Elmo model have context sensitive characteristics, which is more accurate in the expression of polysemous words, and this is not achieved by the traditional word embedding technology. Then, the model uses the recurrent neural network to extract the deep-seated features of the word vector and fuse the features. Finally, the softmax function is used to realize the sentiment classification of text. Experimental results show that Elmo-RNN can effectively improve the accuracy of text sentiment analysis.