Hidden Topic Variable Graphical Model Based on Deep Learning Framework

The hidden topic variable graphical model represents potential topics or potential topic changes by nodes. The current study of hidden topic variable graphical models suffers from the flaw that they can only extract single level topic nodes. This paper proposes a probabilistic graphical model based on the framework of deep learning to extract multi-level topic nodes. The model adds the preprocessing layer to the bottom of the hidden topic variable graphical model. The preprocessing layer used in the paper is the self-organizing maps (SOM) model. By introducing the SOM, the model can effectively extract different topic status with those extracted by the hidden topic variable graphical model. In addition, the hidden topic variable graphical model used in this paper is constructed by hidden Markov model (HMM) and conditional random field (CRF). In order to make up the short-distance dependency Markov property, we use the characteristic function defined by first-order logic. On this basis, we propose a new algorithm by hierarchically extracting topic status. Experimental results on both the international universal Amazon sentiment analysis dataset and the Tripadvisor sentiment analysis dataset show that the proposed algorithm improves the accuracy of sentiment analysis. And the new algorithm can mine more macroscopic topic distribution information and local topic information.