Sentiment analysis of Chinese microblog based on BiGRU-attention

Abstract

Microblog short text usually contains rich emotional information. It is a hot research topic in network data mining to grasp the dynamics of network public opinion through microblog emotion analysis. In order to improve the effect of Chinese microblog sentiment analysis, this paper first uses word embedding technology to quantify microblog short text from high dimension to low dimension vector space; Then, the global features of microblog data are extracted through BiGRU, and the Attention mechanism is introduced to obtain important features to build a Chinese microblog emotion analysis model. The feasibility and superiority of the model were verified with the public data set released by SMP2020. The accuracy, recall and F1 values of the model reached 78.65%, 78.57% and 78.41% respectively. The experimental results show that the feature vector of BiGRU combined with attention mechanism contains more rich emotion information of short text of microblog, which can effectively improve the performance of sentiment analysis of Chinese microblog. The experimental results show that the feature vector of Bi-GRU combined with the attention mechanism contains richer semantic information of the text, which can effectively improve the performance of emotion recognition of online public opinions.