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

Q3 Decision Sciences
Haixiang He;Shiqi Ma
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引用次数: 0

Abstract

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.
基于深度学习的社交网络负面舆情安全监测与预警
随着社会网络的不断发展,负面社会网络舆情频频出现,对其安全监测预警尤为重要。本文以新浪微博为例,从平台上抓取文本,利用BERT生成词向量,结合双向门通循环单元(BiGRU)和注意机制设计情感倾向分类方法,实现了积极和消极情感文本的分类。然后利用TCN对负面情绪文本进行预测,实现舆情安全监测预警。结果发现BERT的表现最好。与其他深度学习方法相比,BERT-BiGRUA在情绪倾向分类上的P值为0.9431,R值为0.9012,F1值为0.9217,均为最佳。在对负面情绪文本的预测中,TCN比长短期记忆等方法均方误差更小,R^{2}$更高,预测效果更好。结果验证了本文设计的方法在实际安全监测和舆情预警中的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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