An adaptive dual graph convolution fusion network for aspect-based sentiment analysis
Article No.: 76, Pages 1 - 23
Abstract
Aspect-based Sentiment Analysis (ABSA), also known as fine-grained sentiment analysis, aims to predict the sentiment polarity of specific aspect words in the sentence. Some studies have explored the semantic correlation between words in sentences through attention-based methods. Other studies have learned syntactic knowledge by using graph convolution networks to introduce dependency relations. These methods have achieved satisfactory results in the ABSA tasks. However, due to the complexity of language, effectively capturing semantic and syntactic knowledge remains a challenging research question. Therefore, we propose an Adaptive Dual Graph Convolution Fusion Network (AD-GCFN) for aspect-based sentiment analysis. This model uses two graph convolution networks: one for the semantic layer to learn semantic correlations by an attention mechanism, and the other for the syntactic layer to learn syntactic structure by dependency parsing. To reduce the noise caused by the attention mechanism, we designed a module that dynamically updates the graph structure information for adaptively aggregating node information. To effectively fuse semantic and syntactic information, we propose a cross-fusion module that uses the double random similarity matrix to obtain the syntactic features in the semantic space and the semantic features in the syntactic space, respectively. Additionally, we employ two regularizers to further improve the ability to capture semantic correlations. The orthogonal regularizer encourages the semantic layer to learn word semantics without overlap, while the differential regularizer encourages the semantic and syntactic layers to learn different parts. Finally, the experimental results on three benchmark datasets show that the AD-GCFN model is superior to the contrast models in terms of accuracy and macro-F1.
References
[1]
E. Cambria, D. Das, S. Bandyopadhyay, and A. Feraco. 2017. Affective computing and sentiment analysis. A Practical Guide to Sentiment Analysis. Springer, Berlin, 1–10.
[2]
G. Vinodhini and R. M. Chandrasekaran. 2012. Sentiment analysis and opinion mining: A survey. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 6 (2012), 282–292.
[3]
E. Choi, H. Rashkin, L. Zettlemoyer, and Y. Choi. 2016. Document-level sentiment inference with social, faction, and discourse context. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 333–343.
[4]
D. Tang, B. Qin, and T. Liu. 2015. Learning semantic representations of users and products for document level sentiment classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’15). 1014–1023.
[5]
D. Tang, B. Qin, and T. Liu. 2015. Document modelling with gated recurrent neural network for sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’15).
[6]
B. Pang and L. Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Info. Retriev. 2, 1–2 (2008), 1–135.
[7]
Q. Le and T. Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning (ICML’14). 1188–1196.
[8]
M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos. 2015. Semeval-2015 task 12: Aspect based sentiment analysis. In Proceedings of the 9th International Workshop on Semantic Evaluation. 486–495.
[9]
M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. AL-Smadi, M. Al-Ayyoub, Y. Zhao, B. Qin, O. De Clercq, V. Hoste, M. Apidianaki, X. Tannier, N. Loukachevitch, E. Kotelnikov, N. Bel, S. M. Jiménez-Zafra, and G. Eryiğit. 2016. Semeval-2016 task 5: Aspect based sentiment analysis. In Proceedings of the 10th International Workshop on Semantic Evaluation. 19–30.
[10]
C. Du, H. Sun, J. Wang, Q. Qi, J. Liao, T. Xu, and M. Liu. 2019. Capsule network with interactive attention for aspect-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing. 5488–5497.
[11]
Z. Chen and T. Qian. 2019. Transfer capsule network for aspect level sentiment classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics. 547–556.
[12]
Shiguan Pang, Yun Xue, Zehao Yan, Weihao Huang, and Jinhui Feng. 2021. Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis. In Findings of the Association for Computational Linguistics (ACL-IJCNLP’21). 2627–2636.
[13]
Zheng Zhang, Zili Zhou, and Yanna Wang. SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4916–4925.
[14]
Z. Wu and D. C. Ong. 2021. Context-guided BERT for targeted aspect-based sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence. 14094–14102.
[15]
F. Yuan, L. Zhang, X. Xia, Q. Huang, and X. Li. 2020. A wave-shaped deep neural network for smoke density estimation. IEEE Trans. Image Process. 29, (2020), 2301–2313.
[16]
F. Yuan, L. Zhang, X. Xia, Q. Huang, and X. Li. 2021. A gated recurrent network with dual classification assistance for smoke semantic segmentation. IEEE Trans. Image Process. 30 (2021), 4409–4422.
[17]
Y. Wang, M. Huang, X. Zhu, and L. Zhao. 2016. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 606–615.
[18]
D. Ma, S. Li, X. Zhang, and H. Wang. 2017. Interactive attention networks for aspect-level sentiment classification. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 4068–4074.
[19]
P. Chen, Z. Sun, L. Bing, and W. Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the Conference on Empirical methods in Natural Language Processing. 452–461.
[20]
F. Fan, Y. Feng, and D. Zhao. 2018. Multi-grained attention network for aspect-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 3433–3442.
[21]
L. Li, Y. Liu, and A. Q. Zhou. 2018. Hierarchical attention based position-aware network for aspect-level sentiment analysis. In Proceedings of the 22nd Conference on Computational Natural Language Learning. 181–189.
[22]
X. Li, L. Bing, W. Lam, and B. Shi. 2018. Transformation networks for target-oriented sentiment classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 946–956.
[23]
C. Sun, L. Huang, and X. Qiu. 2019. Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 380–385.
[24]
Z. Gao, A. Feng, X. Song, and X. Wu. 2019. Target-dependent sentiment classification with BERT. IEEE Access 7 (2019), 154290–154299.
[25]
S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.12.
[26]
K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724–1734.
[27]
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).
[28]
L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 49–54.
[29]
R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier. 2018. Effective attention modeling for aspect-level sentiment classification. In Proceedings of the 27th International Conference on Computational Linguistics. 1121–1131.
[30]
K. Sun, R. Zhang, S. Mensah, Y. Mao, and X. Liu. 2019. Aspect-level sentiment analysis via convolution over dependency tree. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 5679–5688.
[31]
K. Wang, W. Shen, Y. Yang, X. Quan, and R. Wang. 2020. Relational graph attention network for aspect-based sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3229–3238.
[32]
H. Tang, D. Ji, C. Li, and Q. Zhou. 2020. Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6578–6588.
[33]
R. Li, H. Chen, F. Feng, Z. Ma, X. Wang, and E. Hovy. 2021. Dual graph convolutional networks for aspect-based sentiment analysis. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 6319–6329.
[34]
Walaa Medhat, hmed Hassan, and Hoda Korashy. 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 5, 4 (2014), 1093–1113.
[35]
M. Hu and B. Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 168–177.
[36]
B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing. 79–86.
[37]
S. Moghaddam and M. Ester. 2010. Opinion digger: An unsupervised opinion miner from unstructured product reviews. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 1825–1828.
[38]
J. Du, L. Gui, and R. Xu. 2016. Extracting opinion expression with neural attention. In Proceedings of the 5th of Chinese National Conerence. on Social Media Processing (SMP’16). 151–161.
[39]
C. Zhang. 2008. Automatic keyword extraction from documents using conditional random fields. J. Comput. Info. Syst. 4, 3 (2008), 1169–1180.
[40]
C. Lin, Y. He, R. Everson, and S. Ruger. 2012. Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowledge Data Eng. 24, 6 (2012), 1134–1145.
[41]
Y. Jo and A. H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 815–824.
[42]
A. Chatterjee, U. Gupta, M. K. Chinnakotla, R. Srikanth, M. Galley, and Agrawal. 2019. Understanding emotions in text using deep learning and big data. Comput. Hum. Behav. 93 (2019), 309–317.
[43]
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Hierarchical attention networks for document classification. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480–1489.
[44]
Shuang Wen and Jian Li. 2018. Recurrent convolutional neural network with attention for twitter and yelp sentiment classification: ARC model for sentiment classification. In Proceedings of the International Conference on Algorithms, Computing, and Artificial Intelligence.
[45]
Gang Liu and Jiabao Guo. 2019. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337 (2019), 325–338.
[46]
M. E. Basiri, S. Nemati, M. Abdar, E. Cambria, and U. R. Acharya. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Gen. Comput. Syst. 115, 279–294.
[47]
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171–4186.
[48]
J. Zhou, J. X. Huang, Q. V. Hu, and L. He. 2020. Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl.-Based Syst. 205 (2020), 106292.
[49]
Y. Song, J. Wang, T. Jiang, Z. Liu, and Y. Rao. 2019. Attentional encoder network for targeted sentiment classification. Retrieved from http://arxiv.org/abs/1902.09314
[50]
Zeguan Xiao, Jiarun Wu, Qingliang Chen, and Congjian Deng. BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 913–920.
[51]
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. 2019. Roberta: A robustly optimized BERT pretraining approach. Retrieved from https://arXiv:1907.11692
[52]
Z. Lan, M. Chen, S. Goodman, K. P. Sharma, and R. Soricut. 2019.Albert: A lite BERT for self-supervised learning of language representations. Retrieved from https://arXiv:1909.11942
[53]
Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45, 11, 263–268.
[54]
Z. Zhang, P. Cui, and W. Zhu. 2020. Deep learning on graphs: A survey. IEEE Trans. Knowl. Data Eng. 34, 1 (2020), 249–270.
[55]
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst. 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30, 3 (2013), 83–98.
[56]
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip. 2020. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 1 (2020) 4–24.
[57]
K. Mrini, F. Dernoncourt, T. Bui, W. Chang, and N. Nakashole. 2019. Rethinking Self-Attention: An Interpretable Self-Attentive Encoder-Decoder Parser. Retrieved from http://arxiv.org/abs/1902.09314
[58]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008.
[59]
Z. hang, J. Bu, M. Ester, J. Zhang, C. Yao, Z. Yu, and C. Wang. 2019. Hierarchical graph pooling with structure learning. Retrieved from https//arXiv:1911.05954
[60]
L. Pylkkänen. 2019. The neural basis of combinatory syntax and semantics. Science 366, 6461 (2019), 62–66.
[61]
R. P. Adams and R. S. Zemel. 2011. Ranking via Sinkhorn Propagation. Retrieved from https://arxiv.org/abs/1106.1925
[62]
R. Santa Cruz, B. Fernando, A. Cherian, and S. Gould. 2018. Visual permutation learning. IEEE Trans. Pattern Anal. Mach. Intell. 41, 12 (2018), 3100–3114.
[63]
R. Wang, J. Yan, and X. Yang. 2019. Learning combinatorial embedding networks for deep graph matching. In Proceedings of the IEEE/C VF International Conference on Computer Vision. 3056–3065.
[64]
M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. AL-Smadi, and G. Eryiğit. 2014. SemEval-2014 task4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval’14). 27–35.
[65]
J. Pennington, R. Socher, and C. D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1532–1543.
[66]
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Retrieved from https://arXiv:1412.6980
[67]
Chen Zhang, Qiuchi Li, and Dawei Song. Aspect-based sentiment classification with aspect specific graph convolutional networks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 4568–4578.
[68]
Mi Zhang and Tieyun Qian. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 3540–3549.
[69]
Chenhua Chen, Zhiyang Teng, and Yue Zhang. Inducing target-specific latent structures for aspect sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 5596–5607.
Index Terms
- An adaptive dual graph convolution fusion network for aspect-based sentiment analysis
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Publication History
Published: 21 June 2024
Online AM: 17 April 2024
Accepted: 04 April 2024
Revised: 05 February 2024
Received: 03 October 2023
Published in TALLIP Volume 23, Issue 6
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