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Volume 44 Issue 3
Mar.  2022
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WANG Ruyan, TAO Zhongyuan, ZHAO Rongjian, ZHANG Puning, YANG Zhigang. Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1111-1118. doi: 10.11999/JEIT210459
Citation: WANG Ruyan, TAO Zhongyuan, ZHAO Rongjian, ZHANG Puning, YANG Zhigang. Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1111-1118. doi: 10.11999/JEIT210459

Multi-interaction Graph Convolutional Networks for Aspect-level Sentiment Analysis

doi: 10.11999/JEIT210459
Funds:  The National Natural Science Foundation of China (61901071, 61871062, 61771082), The University Innovation Research Group Program of Chongqing (CXQT20017), The Natural Science Foundation of Chongqing (cstc2019jcyj-zdxmX0024)
  • Received Date: 2021-05-25
  • Rev Recd Date: 2021-09-07
  • Available Online: 2021-09-17
  • Publish Date: 2022-03-28
  • Aspect level sentiment analysis aims to identify the sentiment polarity of a specific aspect in a given context, and is a fine-grained sentiment analysis task. The traditional attention-based approach, which only performs the semantic interaction between words, does not establish the syntactic relation interaction between aspect words and text words, resulting in the aspect words incorrectly focusing on information about words that are irrelevant to their syntax. In addition, the positional distance feature and the syntactic distance feature of words, which reflect their relationships in the linear form of the sentence and in the syntactic dependency tree of the sentence, respectively, are ignored by the method of processing syntactic information using graph convolutional networks, allowing irrelevant information far from the aspect words to interfere with their sentiment analysis. To address this problem, a Multi-Interaction Graph Convolutional Network (MIGCN) is proposed. First, the context words positional distance features are fed into each layer of the graph convolutional network, while the adjacency matrix of the graph convolutional network is weighted by using the syntactic distance of context words in the dependency tree. Finally, semantic interaction and syntactic interaction are designed to process the semantic and syntactic information between words, respectively. The experimental results show the proposed model can outperform state-of-the-art baselines on the available datasets.
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  • [1]
    JIANG Long, YU Mo, ZHOU Ming, et al. Target-dependent twitter sentiment classification[C]. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, USA, 2011: 151–160.
    [2]
    TANG Duyu, QIN Bing, FENG Xiaocheng, et al. Effective LSTMs for target-dependent sentiment classification[C]. The 26th International Conference on Computational Linguistics, Osaka, Japan, 2016: 3298–3307.
    [3]
    MA Dehong, LI Sujian, ZHANG Xiaodong, et al. Interactive attention networks for aspect-level sentiment classification[C]. The 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017: 4068–4074.
    [4]
    FAN Feifan, FENG Yansong, and ZHAO Dongya Multi-grained attention network for aspect-level sentiment classification[C]. The 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018: 3433–3442.
    [5]
    KIPF T N and WELLING M. Semi-supervised classification with graph convolutional networks[C]. The 5th International Conference on Learning Representations, Toulon, France, 2017.
    [6]
    ZHANG Chen, LI Qiuchi, and SONG Dawei. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]. The 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019: 4568–4578.
    [7]
    HUANG Binxuan and CARLEY K M. Syntax-aware aspect level sentiment classification with graph attention networks[C]. The 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019: 5469–5477.
    [8]
    PHAN M H and OGUNBONA P O. Modelling context and syntactical features for aspect-based sentiment analysis[C]. The 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020: 3211–3220.
    [9]
    XUE Wei and LI Tao. Aspect based sentiment analysis with gated convolutional networks[C]. The 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018: 2514–2523.
    [10]
    SUN Kai, ZHANG Richong, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree[C]. The 2019 Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019: 5679–5688.
    [11]
    BAI Xuefeng, LIU Pengbo, and ZHANG Yue. Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 503–514. doi: 10.1109/TASLP.2020.3042009
    [12]
    ZHOU Li, WANG Tingyu, QU Hong, et al. A weighted GCN with logical adjacency matrix for relation extraction[C]. The 24th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, 2020: 2314–2321.
    [13]
    DONG Li, WEI Furu, TAN Chuanqi, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification[C]. The 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, USA, 2014: 49–54.
    [14]
    PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. Semeval-2014 task 4: Aspect based sentiment analysis[C]. The 8th International Workshop on Semantic Evaluation, Dublin, Ireland, 2014: 27–35.
    [15]
    PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. Semeval-2015 task 12: Aspect based sentiment analysis[C]. The 9th International Workshop on Semantic Evaluation, Denver, USA, 2015: 486–495.
    [16]
    PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. Semeval-2016 task 5: Aspect based sentiment analysis[C]. The 8th International Workshop on Semantic Evaluation, San Diego, USA, 2016: 19–30.
    [17]
    SONG Youwei, WANG Jiahai, JIANG Tao, et al. Targeted sentiment classification with attentional encoder network[C]. 28th International Conference on Artificial Neural Networks and Machine Learning, Munich, Germany, 2019: 93–103.
    [18]
    ZHANG Mi and QIAN Tieyun. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]. The 2020 Conference on Empirical Methods in Natural Language Processing, Online, 2020: 3540–3549.
    [19]
    CHEN Chenhua, TENG Zhiyang, and ZHANG Yue. Inducing target-specific latent structures for aspect sentiment classification[C]. The 2020 Conference on Empirical Methods in Natural Language Processing, Online, 2020: 5596–5607.
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