Multi-Information Enhanced Graph Convolutional Network For Aspect Sentiment Analysis

doi:10.3778/j.issn.1002-8331.2305-0376

Abstract

Abstract: Aspect level sentiment analysis aims to predict the emotional polarity of specific aspects of a sentence.However, there is still the problem of insufficient use of semantic information in the current stage of research, on the one hand, most of the existing work focuses on learning the dependency information between contextual words and aspect words, and does not make full use of the semantic information of sentences; on the other hand, the existing research does not focus on the syntax construction of dependency trees, so it does not make full use of the grammatical structure information to supplement the semantic information. In view of the above problems, this paper proposes a multi-information augmented graph convolutional neural network (MIE-GCN) model. It mainly includes two parts: one is to form a multi-information fusion layer through aspect perception attention, self-attention and external common sense to make full use of semantic information; the second is to construct a grammatical mask matrix of sentences according to the different grammatical distances between words, and supplement semantic information by obtaining comprehensive grammatical structure information. Finally, the graph convolutional neural network is used to enhance the node representation. The experimental results on the benchmark dataset show that the proposed model has a certain improvement over the comparison model.

Key words: aspect-based sentiment analysis, external common sense, aspect perception attention, syntactic mask matrix

摘要： 方面级情感分析旨在预测句子中特定方面的情感极性。然而，现阶段的研究依然存在语义信息不充分利用的问题，一方面大多数现有工作侧重于学习上下文词到方面词之间的依存信息，没有充分利用句子的语义信息；另一方面现有研究没有专注于依存树的语法构建，从而没有充分利用语法结构信息去补充语义信息。针对以上问题，提出多信息增强图卷积神经网络（MIE-GCN）模型。主要包括两部分：一是通过方面感知注意力、自注意力和外部常识形成多信息融合层充分利用语义信息；二是根据单词间不同的语法距离构造句子的语法掩码矩阵，通过获得全面语法结构信息来补充语义信息。利用图卷积神经网络增强节点表示。在基准数据集上的实验结果表明，提出的模型均比对比模型有一定的提升。

关键词: 方面级情感分析, 外部常识, 方面感知注意力, 语法掩码矩阵

YANG Chunxia, YAN Han, WU Yalei, HUANG Yukun. Multi-Information Enhanced Graph Convolutional Network For Aspect Sentiment Analysis[J]. Computer Engineering and Applications, 2024, 60(14): 144-151.

杨春霞, 闫晗, 吴亚雷, 黄昱锟. 用于方面级情感分析的多信息增强图卷积网络[J]. 计算机工程与应用, 2024, 60(14): 144-151.

References

[1] HU M Q, LIU B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004: 168-177.
[2] SUN K, ZHANG R, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 5679-5688.
[3] HUANG B, CARLEY K M. Syntax-aware aspect level sentiment classification with graph attention networks[J]. arXiv:1909.02606, 2019.
[4] JIANG L, YU M, ZHOU M, et al. Target-dependent twitter sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011: 151-160.
[5] WANG K, SHEN W, YANG Y, et al. Relational graph attention network foraspect-based sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 3229-3238.
[6] WANG Y, HUANG M, ZHU X, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016: 606-615.
[7] CHEN P, SUN Z, BING L, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 452-461.
[8] WANG J, LI J, LI S, et al. Aspect sentiment classification with both word-level and clause-level attention networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018: 4439-4445.
[9] ZHANG M, QIAN T. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020: 3540-3549.
[10] REN Z, ZENG G, LIU C, et al. A lexicon-enhanced attention network for aspect-level sentiment analysis[J]. IEEE Access, 2020, 8: 93464-93471.
[11] DISTANTE D, FARALLI S, RITTINGHAUS S, et al. DomainSenticNet: an ontology and a methodology enabling domain-aware sentic computing[J]. Cognitive Computation, 2022, 14: 62-77.
[12] XING F Z, PALLUCCHINI F, CAMBRIA E. Cognitive-inspired domain adaptation of sentiment lexicons[J]. Information Processing and Management, 2019, 56(3): 554-564.
[13] DEVLIN J, CHANG M, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[14] ZHANG C, LI Q, SONG D. Aspect-based sentiment classification with aspect-specific graphConvolutional networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 4567-4577.
[15] FAN F, FENG Y, ZHAO D. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018: 3433-3442.
[16] LI D, WEI F, TAN C, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014: 49-54.
[17] PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 task 4: aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation, 2014: 27-35.
[18] TANG D, QIN B, FENG X, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 3298-3307.
[19] MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017: 4068-4074.
[20] CHEN C, TENG Z, ZHANG Y. Inducing target-specific latent structures or aspect sentiment classification[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020: 5596-5607.
[21] 王汝言, 陶中原, 赵荣剑, 等. 多交互图卷积网络用于方面情感分析[J]. 电子与信息学报, 2022, 44(3): 1111-1118.
WANG R Y, TAO Z Y, ZHAO R J, et al. Multi interaction graph convolutional networks for aspect sentiment analysis[J]. Journal of Electronics and Information Technology, 2022, 44 (3): 1111-1118.
[22] 杨先凤, 汤依磊, 李自强. 基于交替注意力机制和图卷积网络的方面级情感分析模型[J]. 计算机应用, 2024, 44(4): 1058-1064.
YANG X F, TANG Y L, LI Z Q. Aspect level sentiment analysis model based on alternating attention mechanism and graph convolutional network[J]. Computer Applications, 2024, 44(4): 1058-1064.
[23] ZHOU J, HUANG, HU Q V, et al. SK-GCN: modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification[J]. Knowledge-Based Systems, 2020, 205(3): 214-223.