Aspect-Level Sentiment Analysis Model Combining Double Graph Convolution and Gated Linear Unit

doi:10.19678/j.issn.1000-3428.0067557

Abstract

Abstract:

The aspect-level sentiment analysis aims to determine the sentiment polarity of a given aspect of a sentence. The existing graph neural network-based aspect-level sentiment analysis has two shortcomings: first, it ignores different types of syntactic dependencies and word co-occurrence information in the corpus; second, it cannot accurately control the flow of sentiment information to a given aspect. To address these problems, this study proposes an aspect-level sentiment analysis model that combines dual graph convolution and a Gated Linear Unit(GLU). The model first uses the global vocabulary map to encode word co-occurrence information in the corpus, and thereafter uses the classification summary structure to distinguish the frequency of co-occurrence of various words and different types of syntactic dependencies on the vocabulary and syntax maps. Double-layer convolution is thereafter performed on the two graphs, and the BiAffine transform module is used as a bridge to effectively exchange relevant features between the two Graph Convolution Network(GCN) modules, thus effectively integrating syntactic and lexical information. Finally, the GLU is used to control the flow of sentiment information to a given aspect such that the model can focus more on analyzing the sentiment information related to this aspect and avoid irrelevant sentiment information from affecting the sentiment analysis results of a given aspect, thus improving the accuracy of the analysis. The experimental results demonstrate that on the four datasets of Twitter, Laptop14, Restaurant15, and Restaurant16, the accuracy of the model reached 74.82%, 77.61%, 82.29%, and 89.81%, respectively, and the F1 value reached 72.97%, 73.52%, 67.72%, and 73.37%, respectively. The aspect-level sentiment classification performance is significantly better than those of the other baseline models.

Key words: aspect-level sentiment analysis, word co-occurrence information, double graph convolution, information interaction, Gated Linear Unit(GLU)

摘要：

方面级情感分析旨在确定句子中给定方面的情感极性。现有的基于图神经网络的方面级情感分析存在以下2个方面的不足: 忽略了不同类型的句法依存关系和语料库中的词共现信息, 以及不能准确地控制情感信息流向给定方面。针对以上问题, 提出融合双图卷积与门控线性单元(GLU)的方面级情感分析模型。该模型首先采用全局词汇图来编码语料库中的词共现信息, 在词汇图和句法图上利用分类概括结构来区分各种词共现频率和不同类型的句法依存关系。然后分别在2个图上进行双层卷积, 继而使用BiAffine变换模块作为桥梁, 在2个图卷积网络模块之间有效地交换相关特征, 从而有效地融合句法信息和词汇信息。最后利用GLU控制情感信息流向给定方面, 使模型可以更专注地分析与该方面相关的情感信息, 避免不相关的情感信息影响对给定方面的情感分析结果, 从而提高分析的准确性。实验结果表明, 在Twitter、Laptop14、Restaurant15和Restaurant16数据集上, 该模型的准确率分别达到74.82%、77.61%、82.29%和89.81%, F1值分别达到72.97%、73.52%、67.72%和73.37%, 方面级情感分类效果明显优于其他基线模型。

关键词: 方面级情感分析, 词共现信息, 双图卷积, 信息交互, 门控线性单元

Chunxia YANG, Yalei WU, Han YAN, Yukun HUANG. Aspect-Level Sentiment Analysis Model Combining Double Graph Convolution and Gated Linear Unit[J]. Computer Engineering, 2024, 50(4): 141-149.

杨春霞, 吴亚雷, 闫晗, 黄昱锟. 融合双图卷积与门控线性单元的方面级情感分析模型[J]. 计算机工程, 2024, 50(4): 141-149.

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Figures/Tables 8

References 21

1	WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2016: 606-615.
2	KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2014: 1746-1751.
3	TANG D Y, QIN B, LIU T. Aspect level sentiment classification with deep memory network[C]//Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2016: 214-224.
4	ZHANG M, QIAN T Y. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2020: 3540-3549.
5	ZHANG C, LI Q C, SONG D W. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2019: 4567-4577.
6	HUANG B X, CARLEY K. Syntax-aware aspect level sentiment classification with graph attention networks[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2019: 5469-5477.
7	SUN K, ZHANG R C, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2019: 5679-5688.
8	TAY Y, TUAN L A, HUI S C. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis[C]//Proceedings of AAAI Conference on Artificial Intelligence. [S. l. ]: AAAI Press, 2018: 5956-5963.
9	HOCHREITER S, SCHMIDHUBER J. Long short-term memory. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735
10	DAUPHIN Y N, GRANGIER D. Predicting distributions with linearizing belief networks[EB/OL]. [2023-01-02]. https://arxiv.org/pdf/1511.05622.pdf.
11	田乔鑫, 孔韦韦, 滕金保, 等. 基于并行混合网络与注意力机制的文本情感分析模型. 计算机工程, 2022, 48 (8): 266- 273. URL
	TIAN Q X, KONG W W, TENG J B, et al. Text sentiment analysis model based on parallel hybrid network and attention mechanism. Computer Engineering, 2022, 48 (8): 266- 273. URL
12	刘佳, 王潇, 王红旗. 基于LSTM的中文微博情感分析方法. 计算机工程与应用, 2020, 56 (13): 113- 119. URL
	LIU J, WANG X, WANG H Q. LSTM based sentiment analysis method for Chinese Weibo. Computer Engineering and Applications, 2020, 56 (13): 113- 119. URL
13	DONG L, WEI F R, TAN C Q, 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. Stroudsburg, USA: Association for Computational Linguistics, 2014: 49-54.
14	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. Stroudsburg, USA: Association for Computational Linguistics, 2014: 27-35.
15	PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2015 task 12: aspect based sentiment analysis[C]//Proceedings of the 9th International Workshop on Semantic Evaluation. Stroudsburg, USA: Association for Computational Linguistics, 2015: 486-495.
16	PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2016 task 5: aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation. San Diego, USA: [s. n. ], 2016: 19-30.
17	TANG D Y, QIN B, FENG X C, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. Osaka, Japan: [s. n. ], 2016: 3298-3307.
18	CHEN P, SUN Z Q, BING L D, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2017: 452-461.
19	HUANG B X, OU Y L, CARLEY K M. Aspect level sentiment classification with attention-over-attention neural networks[C]//Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Washington D. C., USA: IEEE Press, 2018: 197-206.
20	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. Melbourne, Australia: International Joint Conferences on Artificial Intelligence Organization, 2017: 4068-4074.
21	CHEN C H, TENG Z Y, ZHANG Y. Inducing target-specific latent structures for aspect sentiment classification[C]//Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2020: 5596-5607.

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