陈永恒;姚桂杰;林耀进;

• 1:闽南师范大学计算机学院

• 2:中国石油天然气股份有限公司大港石化分公司

摘要(Abstract)：

自动发现话题的隐含结构、情感的极性及其关系,可以方便用户从海量网络评论集中快速获得他们关注的主要观点.提出一种基于非监督式的层次话题的情感(Unsupervised Level Aspect-Sentiment,ULAS)模型,利用贝叶斯非参数性模型作为先验知识,实现非监督式发现未标记评论文本集话题的层次结构,分析层次话题的情感极性.实验结果表明,相比传统的JST和ASUM模型,ULAS模型具备较高的分类精确度和较强的模型泛化能力,能够解决传统话题情感模型只能在单一粒度话题层进行情感分析的问题,实现多粒度话题层的情感分析,满足用户对于评论对象不同粒度话题的情感信息需求.

关键词(KeyWords)：非监督式层次话题情感模型;隐藏狄利克雷分配;文本分析;网络评论;主题发现;主题模型;非参贝叶斯模型

Abstract:

Keywords:

基金项目(Foundation):国家自然科学基金项目(60373099,60973040,61303131);; 福建省教育厅科技A类项目(JA13196)

作者(Author):陈永恒;姚桂杰;林耀进;

Email:

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