Abstract: Aspect-level sentiment classification is to analyze the sentiment polarity in a given aspect for a given text.In the exis-ting mainstream solutions,the attention mechanism-based cyclic neural network model ignores the importance of keyword pro-ximity context information,and the CNN multilayer model is not good at capturing sentence-level long-distance dependency information.This paper proposes an aspect-level emotion classification network model based on disconnected gated recurrent units(DGRU) and attention mechanism,which is abbreviated as ATT-DGRU.The DGRU network used in this model integrates the advantages of circular neural network and CNN.It can not only capture the long-distance dependent semantic information of text,but also extract the semantic information of key phrases.Attention mechanism is used to capture the importance of each word to a specific aspect when deducing the sentiment polarity of a specific aspect,meanwhile generates an emotional weight vector,which can be visualized.Accuracies of two-class and three-class of ATT-DGRU model constructed in this paper can reach 91.11% and 87.76% respectively in ACSA task on Chinese hotel review datasets.Accuracies of two-class and three class of ATT-DGRU model can reach 77.21% and 90.06% respectively in ATSA task on SemEval2014-Restaurant dataset.

Key words: Aspect-level sentiment classification, Deep learning, Disconnected gated recurrent unit