基于领域识别的主题模型观点挖掘研究

网络新媒体的快速发展,使得网上评论数据呈现爆炸性增长,面对数量庞大的网络文本,使用传统的人工方式来提取观点会导致效率低下、分类界限模糊、领域适应性差等问题。为解决以上问题,在对传统LDA模型进行改进的基础上,提出了一个基于领域判别的LDA主题模型来对在线评论进行观点挖掘。首先,在标准LDA模型中引入领域层,对语料库中的文档采样领域标签,利用领域化的参数来求解LDA模型;其次,考虑到句子间的情感从属关系,在主题层和单词层之间加入情感层,并引入情感转移变量进行表示,提高了情感极性分析的精度,实验结果表明了本文所提模型和理论的有效性。

Opinion mining research on topic modelbased on domain identification

With the rapid development of new network media, the quantity of online reviews has a tendency of explosive growth. Traditional manual methods for opinion mining have some problems when dealing with tremendous online texts, such as low efficiency, fuzzy classification boundary, and limited domain adaption ability. In order to solve the above problems, we improve the traditional latent Dirichlet allocation (LDA) model, and propose a LDA topic model based on domain identification for opinion mining of online reviews. Firstly, a domain layer is added to the standard LDA model to sample the domain tags of the document, and field parameters are utilized to solve the LDA model. Secondly, given the sentimental connection between sentences, we insert a sentiment layer between the topic layer and word layer. Sentimental transition variable is introduced to denote related characters, which can increase the accuracy of sentiment polarity analysis. Experimental results verify the validity of the proposed model and theory.