中文摘要：

在对标准潜在狄利克雷分布(LDA)模型进行改进的基础上,提出了一个主题情感混合最大熵LDA模型对在线评论进行细粒度观点挖掘.首先,在传统LDA模型中加入最大熵组件来区分背景词、特征词和观点词,并对特征词和观点词进行局部和全局的划分;然后,在主题层和单词层之间加入情感层,将传统的LDA三层模型扩展成四层;最后,进行情感极性分析,同时获取整篇评论和每个主题的情感极性,生成细粒度的主题情感摘要.实验验证了所提模型和理论的有效性.

英文摘要：

On the basis of improving standard latent Dirichlet allocation (LDA)model,a topic and sentiment hybrid maximum entropy LDA model was proposed for fine-grained opinion mining of online reviews.Firstly,a maximum entropy component was added to the traditional LDA model to distin-guish background words,aspect words and opinion words.Both the local and global division of aspect words and opinion words can be further realized.Secondly,a sentiment layer was inserted between topic layer and word layer.The proposed model was extended from three layers to four layers.Final-ly,sentiment polarity analysis was done to simultaneously acquire the sentiment polarity of the whole review and each topic.Under this case,fine-grained topic-sentiment abstract can be concluded.The related experimental results verify the validity of the proposed model and theory.