关键词: 潜在Dirichlet分配（LDA）模型, 情感词耦合, 隐马尔科夫模型（HMM）, 文本情感建模

Abstract: Against to the problem that traditional topic model cannot obtain emotional information of text well for emotional classification, this paper proposes a latent Dirichlet allocation (LDA) model of emotion. By the text emotion modeling analysis, this paper also proposes an LDA model based on emotional word coupling relationship. The model considers not only the topic of emotional word context, but also the word coupling relationship. It controls the probability distribution of the emotional word by introducing emotional variables, and uses hidden Markov model to analyze the transformation of emotional word coupling relationship. The experiments show that, when analyzing the emotional word coupling relationship and topic at the same time, the model not only is effective for text sentiment modeling, but also can enhance the accuracy of sentiment classification results.

Key words: latent Dirichlet allocation (LDA) model, emotional word coupling, hidden Markov model (HMM), text sentiment modeling