Analysis Model of Epidemic Speech based on BiLSTM and MCNN Structure

An analysis model of epidemic speech based on BiLSTM and MCNN structure is proposed in order to know the news and information about COVID-19 in time and the views and focus of citizens on the situation. BERT pre-training model is used to extract word vectors, and then the information of bidirectional long-term and short-term memory network and convolution neural network models at different levels is fused. Finally, the speech is classified into two categories, and whether its emotion is positive or negative is calculated. Experimental results show that this model can better classify the polarity of speech emotion than the previous word vector model and the traditional neural network model. © 2021 IEEE.