基于Multi-agent理论的社会网络文体分类方法

Social Network Style Classification Method Based on Multi-agent Theory

针对当前社会网络中的文体分类存在分类效果不理想问题,结合网络文体的多样性、多归属性及动态性的特征,提出了一种基于multi-agent的属性融合和词库关联的网络文体分类方法.首先提取网络文体的特征关键词和词义等基本属性,建立Multi-agent的融合分类模型,并给出了基于Multi-agent的社会网络文体融合分类算法.实验结果表明该方法与传统单分类器以及其他多分类器融合分类方法相比,不仅可以通过语义特征提取对语义网络中的网络文体进行高精度分类,而且可以实现社会网络文体分类的自动化,具有更高的分类精度与稳定性.

Recently, there are some problems like extracting hardly and lacking classification methods in stylistic classification of social networks. Combining network stylistic diversity, multi-attribution and dynamic characteristics .A attribute fusion and thesaurus associated method based multi-agent has been proposed from feature extraction. Firstly, it extracts the basic attributes of keywords and meaning of characteristics. Then, a multi-agent fusion classification model has been established with the interaction of multi-agent and it also gives the algorithm of the model. The experimental results show that this method which compares with the traditional single fusion classification classifier and other multi-classifier fusion classification not only achieves the high-precision network stylistic classification in semantic network through Semantic features extraction but also receives Social Network stylistic classification’s automation. The method has a higher accuracy classification and stability.