Application of BERT+Attention Model in Emotion Recognition of Metizens during Epidemic Period
Journal of Physics: Conference Series
; 1982(1), 2021.
Article
in English
| ProQuest Central | ID: covidwho-1338611
ABSTRACT
In 2020, SARS-CoV-2 will affect the hearts of people all over the country, and Weibo will become the representative of people expressing their feelings on the Internet. Traditional emotion dictionary and machine learning methods have poor text emotion recognition effect, while BERT pre-training model is based on bidirectional Transformer model, which can better obtain the emotion expressed by the text and effectively improve the accuracy of the model. On the basis of improving BERT pre-training model, attention mechanism is introduced, and the key features are weighted to make emotion classification more accurate. According to the analysis of emotions expressed by netizens on Weibo during the epidemic, compared with textCNN model, BILSTM model and BILSTM+Attention model, the accuracy rate has increased by 6.25%, 4.69% and 2.67% respectively. The overall performance of this model is the best, and it can effectively recognize text emotion.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Journal of Physics: Conference Series
Year:
2021
Document Type:
Article
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