Research on public opinion sentiment classification based on attention parallel dual-channel deep learning hybrid model

Abstract

The research on sentiment classification of online public opinion is helpful to the management and control of online public opinions. In the matter of the problems of previous sentiment analysis research that it is difficult to well capture text sentiment features and to identify words ambiguity, an Attention Parallel Dual-channel Deep Learning Hybrid Model (ADDHM) is proposed. Bidirectional Encoder Representations from Transformers (BERT) is applied to extract semantic features and training text vector representation. Convolutional Neural Network (CNN) and Bidirectional Long Short-term Memory (BiLSTM), introducing the attention mechanism, form a dual-channel model to extract text semantic features so as to enrich the words meaning and improve the classification level. Microblog public opinion is taken as an experiment case and hyperparameters are adjusted to find the optimal hyperparameter combination. Six comparison models are selected to verify the validity of ADDHM on four data sets. The classification accuracy of the proposed model on the four experimental data sets are respectively 96.68%, 88.86%, 89.64% and 92.72%, which are superior to the comparison model, and the ROC curve performance of the model is also the best. The performance of ADDHM is significantly different from that of the comparison models. ADDHM can effectively optimize the expression of text features and enhance the capacity of extracting text sentiment feature. It has better classification effect and is more befitting for sentiment classification of online public opinion comments.