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Tracing Public Opinion Propagation and Emotional Evolution Based on Public Emergencies in Social Networks

机译:社会网络中基于突发公共事件的舆论传播与情绪进化追踪

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Social network has become the main communication platform for public emergencies, and it has also made the public opinion influence spread more widely. How to effectively obtain public opinions from it to guide the healthy development of the society is an important issue that the government and other functional departments are concerned about. However, the interaction and evolution mechanism between the subject and the environment in the public opinion propagation is complicated, and the public and media attention and reaction to the incident are closely linked with the progress of the incident disposal. And public mining corpus has some shortcomings in the distribution of emotional classification. Only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. In fact, from the perspective of public opinion propagation, this paper built the network matrix between Internet users through the forwarding relationship, and used the social network analysis method and the emotion mining analysis technology to study the interaction and evolution mechanism between the subject and the environment in the public opinion propagation, and it studied the role of users in the emotional propagation of social networks. This paper proposed a sentiment analysis method on the micro-blog platform, which expanded the emotional dictionary and took sentence and emoticon and sentence patterns into account, which improved the accuracy of positive and negative classifications and emotional polarity analysis of the micro-blog.
机译:社交网络已成为突发公共事件的主要交流平台,也使舆论影响力得到了更广泛的传播。如何有效地获取公众意见,指导社会的健康发展,是政府及其他职能部门关注的重要问题。但是,舆论传播过程中主体与环境之间的相互作用和演化机制复杂,公众和媒体对事件的关注和反应与事件处置的进展紧密相关。公共采矿语料库在情感分类的分布上存在一些缺陷。只有及时更新人工规则和情感词典资源,它才能很好地处理新的文本数据。实际上,从舆论传播的角度出发,本文通过转发关系建立了互联网用户之间的网络矩阵,并运用社交网络分析方法和情感挖掘分析技术研究了主题与主题之间的互动和演化机制。环境中的舆论传播,并研究了用户在社交网络情感传播中的作用。本文提出了一种在微博平台上进行情感分析的方法,该方法扩展了情感词典,并考虑了句子和表情符号以及句子模式,提高了微博的正负分类和情感极性分析的准确性。

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