基于LDA模型和语义网络的线上文本挖掘方法

Online Text Mining Method Based on LDA Model and Semantic Network

传统线上文本挖掘方法在克服相似性干扰时,需要加入大量约束条件,降低了挖掘准确率。为此,本文提出基于LDA模型和语义网络的线上文本挖掘方法。采用滤波处理法对线上文本信息进行滤波处理,获得线上文本的抗干扰向量,提取线上文本信息的频谱特征。利用语义网络构建线上文本的语义网络挖掘模型,计算线上文本挖掘信息的相似度。基于LDA模型对获取的线上文本阈值进行分类,得到线上文本挖掘权值,将挖掘到的关系词带入到概念属性内,实现线上文本的挖掘。实验结果表明,基于LDA模型和语义网络的线上文本挖掘方法不仅可以提高挖掘精度,还具有更强的收敛性。

The traditional online text mining methods need to add a large number of constraints to overcome the similarity interference,which reduces the mining accuracy.Therefore,this paper proposes an online text mining method based on LDA model and semantic network.The filtering processing method is used to filter the online text information,the anti-interference vector of the online text is obtained,and the spectral characteristics of the text information on the outgoing line are extracted.The semantic network mining model of online text is constructed by using semantic network,and the similarity of online text mining information is calculated.Based on the LDA model,the online text threshold is classified,and the online text mining weight is obtained.The relational words mined in the online text are brought into the conceptual attributes to realize the online text mining.The experimental results show that the online text mining method based on LDA model and semantic network can not only improve the accuracy of online text mining,but also have stronger convergence.