Security Monitoring and Early Warning of Negative Public Opinion on Social Networks Under Deep Learning

With the continuous development of social networks, negative social network public opinion appears frequently, which is particularly important for its safety monitoring and early warning. Taking Sina microblog as an example, this paper crawled texts from the platform, used BERT to generate word vectors, combined the bidirectional gated recurrent unit (BiGRU) and attention mechanism to design an emotion tendency classification method, and realized the classification of positive and negative emotion texts. Then, TCN was used to predict the negative emotion text to realize public opinion safety monitoring and early warning. It was found that BERT had the best performance. Compared with other deep learning methods, BERT-BiGRUA had a P value of 0.9431, an R value of 0.9012, and an F1 value of 0.9217 in the classification of emotion tendency, which were all the best. In the prediction of negative emotion text, TCN obtained a smaller mean square error and a higher $R^{2}$ than long short-term memory and other methods, showing a better prediction effect. The results verify the usability of the approach designed in this paper for practical safety monitoring and early warning of public opinion.