Research on Intelligence Computing Models of Fine-Grained Opinion Mining in Online Reviews

Abstract

In order to obtain evaluation information about the various aspects of products or services, the Fine-grained Topic Sentiment Unification (FG-TSU) model is proposed based on the improvement of LDA (Latent Dirichlet Allocation) model. Firstly, the topics are divided into local and global topics. And the sliding window is introduced to lower co-occurrence information from document to sentence level, to implement fine-grained extraction of local topics. Secondly, the indicator variables are used to distinguish aspects and opinions. Finally, we incorporate the sentiment layer into LDA model to obtain the sentiment polarity of the whole review and specific aspects. The datasets of hotel and mobile phone are selected to verify the domain adaptability of this model. The experimental results verified the feasibility of FG-TSU model in the realization of opinion mining.