Jisuanji kexue (Mar 2022)

Interactive Attention Graph Convolutional Networks for Aspect-based Sentiment Classification

  • PAN Zhi-hao, ZENG Bi, LIAO Wen-xiong, WEI Peng-fei, WEN Song

DOI
https://doi.org/10.11896/jsjkx.210100180
Journal volume & issue
Vol. 49, no. 3
pp. 294 – 300

Abstract

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Aspect-based sentiment classification aims at identifying the sentiment polarity of the given aspect in a sentence.Most of the previous methods are based on long short-term memory network(LSTM)and attention mechanisms,which largely rely on the semantic correlation between aspects and contextual words in the modeled sentence,but ignore the syntactic information in the sentence.To tackle this problem,an interactive attention graph convolutional network(IAGCN) is proposed to model the semantic correlation and syntactic correlation of words in a sentence.Firstly,IAGCN starts with a bi-directional long short-term memory network(BiLSTM) to capture contextual semantic information regarding word orders.Then,the position information is introduced and put it into the graph convolutional network to learn the syntactic information.After that,aspect representation is obtained through mask mechanism.Finally,the interactive attention mechanism is used to interactively calculate and generate the aspect-specific contextual representation as the final classification feature.Through this complementary design,the model can obtain a good contextual representation that aggregates the aspect target information,and is helpful for sentiment classification.Experimental results show that the model achieve a good performance on multiple datasets.Compared with the Bi-IAN model without considering the syntax information,our model are superior to Bi-IAN model on all datasets,especially on the REST14,REST15 and REST16 datasets in the restaurant domain.Our model improves by 4.17%,7.98% and 8.03% on F1 scores respectively compare with the Bi-IAN model.Compared with the ASGCN model,which also takes semantic information and syntax information into account,the F1 scores of our model is better than that of the ASGCN model in all datasets except LAP14 dataset,especially on the REST14,REST15 and REST16 datasets in the restaurant domain.Compared with the ASGCN model,the F1 scores of our model is increased by 2.05%,1.66% and 2.77% respectively.

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