| Summary: | Aspect-based sentiment analysis(ABSA)is a fine-grained sentiment analysis,which aims to predict sentiment polarity of text toward a specific aspect.Currently,given the excellent capabiities of recurrent neural networks(RNN) in sequence mode-ling and the outstanding performance of convolutional neural networks(CNN) in learning local patterns,some works have combined the two to mine sentiment information and achieved good results.However,few works consider aspect information while applying the combination of the two to ABSA.In aspect-based sentiment analysis tasks,most of the work treat aspect as an independent whole interacting with the contexts,but the representation of aspect is too simple and lacks real semantic.To address the above issues,this paper proposes a neural network model based on aspect semantic and gated filtering network(ASGFN) to better mine aspect-based sentiment information.First,an aspect encoding module is designed to capture context-specific aspect semantic information,which is based on a global context fusion multi-head attention mechanism with a graph convolutional neural network to construct aspect representation containing specific semantic.Second,a gated filtering network is designed to connect RNN and CNN as a way to enhance the interaction of aspect with the contexts,while combining the advantages of the RNN and the CNN,and then extracting the sentiment feature.Eventually,the sentiment feature is combined with aspect representation to generate semantic representation that predicts sentiment polarity.Sentiment classification accuracies of 84.72%,78.64%,and 76.22% are achieved in three communal datasets,restaurant,laptop,and twitter,respectively.Experimental results demonstrate the effectiveness of the proposed model,which can improve the performance of ABSA.
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