Microblog sentiment analysis based on ERNIE and dual attention mechanism

Aiming at the problems that the traditional word vector could not represent the polysemy of words in the context, and the priori emotion resources could not be fully utilized in the neural network, a microblog sentiment analysis model based on ERNIE and Dual Attention Mechanism (DAM) was proposed. Firstly, we use the existing emotional resources to construct an emotional resource library including emotional words negative words and adverbs of degree. Secondly, BLSTM network and fully connected network were used to encode the text and the emotional information contained in the text respectively. Different attention mechanisms were used to extract the contextual features and emotional features of the text and the emotional information respectively, and ERNIE pretraining model was used to obtain the dynamic feature representation of the text. Finally, the contextual features and emotional features are spliced and fused to obtain the final feature vector representation. Experimental results show that the classification accuracy of the new model on COAE2014 and weibo_senti_100k data sets reaches 94.50% and 98.23% respectively, which also verifies the effectiveness of applying emotional resources into neural networks.