Analysis Model of Epidemic Speech based on BiLSTM and MCNN Structure
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021
; : 245-249, 2021.
Article
in English
| Scopus | ID: covidwho-1831726
ABSTRACT
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.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021
Year:
2021
Document Type:
Article
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