Aspect-Level Sentiment Analysis Combining Part-of-Speech and External Knowledge.

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]