doi:10.16186/j.cnki.1673-9787.2019100022

Received:2019/10/10

Revised:2019/12/26

Published:2021/01/15

Text sentiment analysis based on multi self-attention and parallel hybrid model

LI Hui1, HUANG Yujie2

1.School of Physics and Electronic Information， Henan Polytechnic University， Jiaozuo  454000 ， Henan， China;2.School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo  454000 ， Henan， China

Abstract:Most of the past studies used a single model for text sentiment analysis， which led to the inability to capture the emotional features of related texts well， and led to the problem of unsatisfactory sentiment analysis. A model of text sentiment analysis method based on self-attention and parallel hybrid model was proposed. First， the Word2vec model was used to capture the semantic features of words and to train word vectors. Secondly ，the double layer multi-head self-attention （ DLMA） was used to learn the word dependence within the text and to capture its internal structural features. The sequence characteristics of the text were then acquired by using a parallel bi-directional gated recurrent unit （ BiGRU ）.Finally， the deep hierarchical feature information was extracted by the improved convolutional neural network （ CNN ）.The model was validated on two data sets， and the accuracy rate reached 92. 71% and 91.08%.The experimental results showed that the method had better learning performance than other models.

Key words:multi-head self-attention;bi-directional gated recurrent unit;convolutional neural network;text

 基于多头自注意力和并行混合模型的文本情感分析_李辉.pdf