基于情感从属和最大熵模型的细粒度观点挖掘

Fine-grained opinion mining based on sentiment dependency and maximum entropy model

目前许多观点挖掘方法挖掘粒度过大,导致反馈信息不足。为解决该问题,对标准LDA模型进行改进,提出主题情感联合最大熵LDA模型进行细粒度观点挖掘。首先,考虑到词的位置和语义信息,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;其次,在主题层和单词层之间加入情感层,实现词语级别的细粒度情感分析,并引入情感转移变量来处理情感从属关系,同时获取整篇评论和每个主题的情感极性,实验验证了所提模型和理论的有效性。

Many current methods of opinion mining are coarse-grained, which are practically problematic due to insufficient feedback information. To address these problems, we propose a novel topic and sentiment joint maximum entropy LDA model in this paper for fine-grained opinion mining. Considering semantic and location information of words, a maximum entropy component is first added to the traditional LDA model to distinguish background words, aspect words and opinion words. Both the local extraction and global extraction of these words are further realized. Secondly, a sentiment layer is inserted between a topic layer and a word layer to perform fine-grained opinion mining on word or phrase level. Transition variable is introduced to deal with sentiment dependency. The sentiment polarity of the whole review and each topic are simultaneously acquired. Experimental results demonstrate the validity of the proposed model and theory.