基于 ELMo 和 Bi-SAN 的中文文本情感分析.

Current sentiment analysis models usually use word2vec, Glo Ve and other methods to generate static word embedding, and traditional convolutional or recurrent depth models cannot fully focus on the context, extract insufficiently features, and reduce the accuracy of sentiment judgment. This paper proposed a Chinese text sentiment analysis model based on ELMo and Bi-SAN. Firstly, through ELMo language model training, the model got the word vector that integrated the word itself and context information to solve the problem of ambiguity of a word. Meanwhile, it used pre-trained skip-gram algorithm to replace the embedding layer of the randomly initialized ELMo model and improved the convergence speed of the model. Then the model used Bi-SAN to extract features. Due to the self-attention mechanism, Bi-SAN could fully focus on the context of each word and extract features more comprehensively. Compared with multiple existing sentiment analysis models, the proposed model achieves higher F, in the hotel review dataset and the NLPCC2014 task2 Chinese dataset, which validates the effectiveness of the model. [ABSTRACT FROM AUTHOR]