Abstract A fine-grained sentiment classification task is to identify the opinion words with the highest degree of correlation with target words and classify the emotional polarity in the text. A deep memory network with multiple-head attention mechanism for aspect level sentiment classification is introduced. The word embedding vector of the text is stored in the memory component, and the multi-head attention mechanism is employed to simultaneously model the overall semantics of the text and the object-related semantics among the multiple feature spaces. A feedforward network layer is applied to integrate the information in multiple feature spaces as a classification feature. Experiments on SemEval-2014 dataset and the extended dataset show that the proposed method is beneficial to alleviate the selective preference of the model.
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2019, Vol. 32 
