摘要
针对多数舆情监控系统对领域知识考虑不足的问题,提出一种基于语义网络的舆情信息分类方法。运用逆文档词频技术拓展公开知识图谱,利用语义网络对舆情信息进行建模,以语义网络中概念节点的映射值表示舆情信息,通过相关概念的互相映射产生增益从而突出舆情信息主题,且可根据舆情信息中的概念发现文中未显式的相关概念,从而反映舆情信息的总体情况。结合主流分类器进行对比实验,结果表明基于语义网络的舆情信息分类技术具有更好的分类效果。
Many public opinion classification system analyze information without enough consideration of domain knowledge,based on this problem, a public opinion classification method based on semantic network is proposed. Expand the public knowledge map by using Term Frequency-Inverse Document Frequency（TFIDF） technology, and use semantic network to model public opinion information. Then, use the mapping values of conceptual nodes in semantic networks to express public opinion information ,the related concepts can be mapped to each other to generate gains, thus highlighting the theme of public opinion information, then will find relevant concepts that are not explicit in public opinion through the concept of public opinion, so as to reflect the overall situation of public opinion information. The comparison experiment with the mainstream classifier shows that the classification method of public opinion information based on the semantic network has better classification effect under the mainstream classification method.
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第3期226-232,共7页
Computer Engineering
基金
上海市科委项目(17511106800)
关键词
舆情信息分类
语义网络
知识图谱
舆情监控系统
概念向量
public opinion information classification
semantic network
knowledge map
public opinion monitoringsystem
concept vector
