一种基于领域语义关系图的短文本实时分析模型

Real-time Analysis Model for Short Texts with Relationship Graph of Domain Semantics

【目的】对网络社区相关舆情领域判别问题进行研究,提出基于领域语义关系图的短文本分析模型,弥补基于知识库和基于机器学习方法的不足,提高模型准确度和时效性。【方法】以高校学生舆情领域为实验对象,从多源网络社区获取478303篇文本数据,结合语义抽取规则,构建总计5248节点、16488条边的高校舆情领域语义关系图;并实现该图的自动扩展;同时,提出一种基于领域语义关系图的短文本分析模型,实现社区文本领域分析。【结果】在文本领域相关性判别方面,本文模型准确度F值最高可达83.94%,相比同等环境中的支持向量机方法、朴素贝叶斯方法和基于深度学习卷积神经网络方法,准确度分别提升8.56%、5.97%、4.27%。【局限】网络社区文本数据量有限;参数反馈机制不够完善。【结论】与基于机器学习方法比较,本文模型准确度有所提升;时效方面,能够达到秒级别处理,实现实时分析。

[Objective]This paper studies the domain discrimination for public opinions of online communities,aiming to improve knowledge base,as well as the effectiveness of the machine learning models.[Methods]We retrieved 478,303 pieces of textual data from multiple online communities for college students.Then,we created a semantic relationship graph with a total of 5,248 nodes and 16,488 edges,which could also be extended automatically.Finally,we proposed a short text analysis model to conduct domain analysis for the texts.[Results]The F value of the proposed model reached 83.94%,which was 8.56%,5.97%and 4.27%higher than those of the SVM,NB and CNN methods.[Limitations]The sample size needs to be expanded and the parameter feedback mechanism needs to be modified.[Conclusions]Compared with methods based on machine learning,the proposed model’s accuracy is improved.It could also conduct real-time analysis.