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The goal of aspect level affective analysis is to identify the affective polarity of specific aspect words in a given sentence. At present, most of the research combining graph convolution neural network and syntactic dependency tree focuses on learning the relationship between context and aspect words according to the sentence dependency tree, but does not focus on the construction of syntactic dependency tree, so it can't make full use of the information on the dependency tree, and will introduce noise. To solve the above problems, this paper proposes a graph convolution network model based on multi-fusion adjacency matrix algorithm. Firstly, external knowledge is used to enhance the role of emotional words in sentences, and the part-of-speech is used for information filtering to remove redundant dependencies in sentences to obtain pruned syntactic dependency trees. The two are combined by multi-fusion adjacency matrix algorithm to obtain syntactic information. The syntactic information and the semantic information extracted from the BiLSTM layer are input into the simplified graph convolution network for feature fusion. Experimental results on five datasets show that the proposed method is effective and can significantly improve the performance of the model. [ABSTRACT FROM AUTHOR]

Fine-grained aspect-based sentiment analysis involves aspect term extraction and aspect sentiment classification. Most existing research methods address them in an independent fashion, which lack a mechanism to account for the relevant information between each other, resulting in training redundancy and waste of resources. To solve the above problems, a joint model based on position embedding and graph convolutional network (PE-GCN) under the framework of multi-task learning is proposed, which is an end-to-end approach to the overall solution of aspect term extraction and aspect sentiment classification. Firstly, the model learns the semantic feature representation of sentence through a bidirectional gated recurrent unit network. Then, it exploits positional embedding to enhance the recognition of aspect terms in sentence, and uses the graph convolutional network to generate a contextual representation containing syntactic information. Finally, interactive attention network is used to model the semantic relationship between context and aspect terms, and the sentiment polarity of aspect terms is output through softmax. Experimental results on the SemEval-2014 public datasets show that the performance of the proposed model has a significant improvement compared with other existing models. [ABSTRACT FROM AUTHOR]

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