User Sentiment Analysis of COVID-19 via Adversarial Training Based on the BERT-FGM-BiGRU Model

Abstract

With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text’s emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation.

Keywords

• sentiment analysis
• COVID-19
• evolution of public opinion
• adversarial training