基于超网络模型的网络舆论本体“噪声”识别

An Opinion-Noise Detection Supernetwork Model

网络舆论的研究既应重视数量也应重视质量,在纷繁复杂的舆论大数据中寻找高质量的网络舆论是对新网络舆论观的践行.以网络舆论质量三维评价体系为理论基础,通过对舆论超网络模型节点和连边的改变和重构,提出了网络舆论本体“噪声”超网络识别模型,涉及环境子网、情绪子网、社交子网和内容子网.网络舆论本体“噪声”是指无法对管理决策的制定提供意见和建议的舆论观点.基于所提出的超网络模型,提取18个特征指标,并利用机器学习分类算法实现对高质量舆论观点的提取.

As looking for high-quality online public opinions from mass reticula data is the practice of the new concept of online public opinion,both quantity and quality aspects should be considered by relevant studies.Based on the three-dimensional evaluation system of the quality of online public opinion,through the change and reconstruction of the nodes and edges of the public opinion supernetwork model,an Opinion-Noise Detection Supernetwork model was proposed,in which there are four subnetworks:Environmental subnetwork,emotional subnetwork,social subnetwork and content subnetwork.The noumenon of online public opinion“noise”refers to the public opinion data that cannot provide suggestions for the formulation of management decisions.Based on the proposed model,18 characteristic indexes were extracted.Finally,by employing machine learning algorithms,the public opinions with high quality were successfully identified.