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Aspect-gated graph convolutional networks for aspect-based sentiment analysis

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Abstract

Aspect-based sentiment analysis aims to predict the sentiment polarity of each specific aspect term in a given sentence. However, the previous models ignore syntactical constraints and long-range sentiment dependencies and mistakenly identify irrelevant contextual words as clues for judging aspect sentiment. In addition, these models usually use aspect-independent encoders to encode sentences, which can lead to a lack of aspect information. In this paper, we propose an aspect-gated graph convolutional network (AGGCN), that includes a special aspect gate designed to guide the encoding of aspect-specific information from the outset and construct a graph convolution network on the sentence dependency tree to make full use of the syntactical information and sentiment dependencies. The experimental results on multiple SemEval datasets demonstrate the effectiveness of the proposed approach, and our model outperforms the strong baseline models.

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Notes

  1. We use spaCy toolkit:https://spacy.io/

  2. Available at: http://alt.qcri.org/semeval2014/task4/

  3. Available at: http://alt.qcri.org/semeval2015/task12/

  4. Available at: http://alt.qcri.org/semeval2016/task5/

  5. Available at: http://goo.gl/5Enpu7

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Acknowledgements

This work was supported in part by the National Social Science Foundation under Award 19BYY076, in part Key R & D project of Shandong Province 2019 JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.

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Authors and Affiliations

  1. School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan, 250357, China

    Qiang Lu, Zhenfang Zhu & Guangyuan Zhang

  2. Chinese Lexicography Research Center, Lu Dong University, Yantai, 264025, China

    Shiyong Kang

  3. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Peiyu Liu

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  1. Qiang Lu
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  2. Zhenfang Zhu
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  3. Guangyuan Zhang
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  4. Shiyong Kang
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  5. Peiyu Liu
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Correspondence to Zhenfang Zhu or Guangyuan Zhang.

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Lu, Q., Zhu, Z., Zhang, G. et al. Aspect-gated graph convolutional networks for aspect-based sentiment analysis. Appl Intell 51, 4408–4419 (2021). https://doi.org/10.1007/s10489-020-02095-3

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