期刊论文

Opinion mining research on topic model based on domain identification

中文

计算机工程与科学

1007-130X

2019

41

7

1297-1302

10.3969/j.issn.1007-130X.2019.07.022

国家自然科学基金（61003192）；

本校为第一机构

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。

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...