SlowGuess/NAACL_2022_OCR huggingface dataset:
To examine whether SSEGCN is able to capture syntax and semantic information for improving ABSA, we conduct case study with a few of sample sentences. Particularly, we compare SSEGCN with ATAE-LSTM, IAN and CDT in Table 4 which contain their predictions and the corresponding truth labels on these sentences. The notations P, N and O in the table represent positive, negative and neutral sentiment, respectively. The first sample "great food but the service was dreadful!" has two aspects ("food" and "service") with contrast sentiment polarities, which may interfere with the prediction of the attention models. The second sample "Biggest complaint is Windows 8." has the interfering word "Biggest", which may neutralize the polarity of the word "complaint". In the third sample, the key point is capturing the negated semantics which is most methods tend to ignore and easily make wrong predictions. The last two examples have no explicit sentiment expression. For the sentence "Not as fact as I would have expect for an i5", CDT does not obtain the representation of the keyword "not" from a syntactic point of view and thus produces wrong prediction. Thus, the ability to learn integral semantics of a sentence is a significant factor for ABSA task. Our SSEGCN correctly predicts all the samples, and the results suggest that SSEGCN effectively combines syntactic structure and semantic information. Additionally, when dealing with complex sentences with implicit sentiment expressions, our SSEGCN can achieve better performance.
To further demonstrate how our SSEGCN improves ABSA task, we use the two test examples in Figure 3 to visualize attention scores. For the sentence
Models	Restaurant	Laptop	Twitter
Accuracy	Macro-F1	Accuracy	Macro-F1	Accuracy	Macro-F1
IAN (Ma et al., 2017)	78.60	-	72.10	-	-	-
RAM (Chen et al., 2017)	80.23	70.80	74.49	71.35	69.36	67.30
TNet (Li et al., 2018)	80.69	71.27	76.54	71.75	74.90	73.60
ASGCN (Zhang et al., 2019)	80.77	72.02	75.55	71.05	72.15	70.40
CDT (Sun et al., 2019)	82.30	74.02	77.19	72.99	74.66	73.66
TD-GAT (Huang and Carley, 2019)	81.2	-	74.0	-	-	-
BiGCN (Zhang and Qian, 2020)	81.97	73.48	74.59	71.84	74.16	73.35
kumaGCN (Chen et al., 2020)	81.43	73.64	76.12	72.42	72.45	70.77
R-GAT (Wang et al., 2020)	83.30	76.08	77.42	73.76	75.57	73.82
DGEDT (Tang et al., 2020)	83.90	75.10	76.80	72.30	74.80	73.40
DualGCN (Li et al., 2021)	84.27	78.08	78.48	74.74	75.92	74.29
Our SSEGCN	84.72	77.51	79.43	76.49	76.51	75.32
BERT (Devlin et al., 2019)	85.97	80.09	79.91	76.00	75.92	75.18
R-GAT+BERT (Wang et al., 2020)	86.60	81.35	78.21	74.07	76.15	74.88
DGEDT+BERT (Tang et al., 2020)	86.30	80.00	79.80	75.60	77.90	75.40
BERT4GCN (Xiao et al., 2021)	84.75	77.11	77.49	73.01	74.73	73.76
T-GCN+BERT (Tian et al., 2021)	86.16	79.95	80.88	77.03	76.45	75.25
Our SSEGCN+BERT	87.31	81.09	81.01	77.96	77.40	76.02
Table 2: Experimental results comparison on three publicly available datasets.
Models	Restaurant	Laptop	Twitter
Accuracy	Macro-F1	Accuracy	Macro-F1	Accuracy	Macro-F1
SSEGCN	84.72	77.51	79.43	76.49	76.51	75.32
w/o self-attention	82.93	75.25	78.32	74.73	75.32	73.22
w/o aspect-aware attention	83.02	75.51	77.85	75.16	75.04	73.29
w/o syntactic mask matrix	83.82	75.60	78.01	74.33	75.63	74.34
w/o aspect-aware attention and syntactic mask matrix	82.75	75.02	77.37	73.35	74.89	72.40
"The staff should be a bit more friendly," our model correctly identifies the sentiment of aspect term "staff" as negative. Our SSEGCN model considers the semantics of the sentence and reduce the attention weight on the word "friendly" through syntactic distance mask and aspect-aware attention. For the harder example of multiple aspects with different sentiment polarities, our model also performs well. For sentence "great food but the service was dreadful!", our model considers the aspect-related semantic correlations by introducing aspect-aware attention and combining syntactic mask matrices. Thus, SSEGCN can accurately find the opinion words corresponding to each aspect term.
4.8 Effect of SSEGCN Layers
In this section, we investigate the effect of the layer number ranging from 1 to 5 on the Restaurant and Laptop datasets. As shown in Figure 4, experimental results show that our model achieves the best performance with 2 layers. First, if the number
of GCN layer is set to 1, SSEGCN can only learn local node information with syntactic distance of 1. Second, node representation will be over-smooth and obtain more redundancy information when the number of GCN layers is large, thus making model in poor performance.
4.9 Effect of Syntax-Mask
In this section, we further analyze the effect of the multiple different syntactic mask matrices on the performance of SSEGCN in Restaurant and Laptop datasets. We conduct different numbers of the syntactic mask matrices from 1 to 7 and the results are demonstrated in Figure 5. We observe that the proposed SSEGCN shows an upward trend with the increase of the number when the number of syntactic mask matrices is less than 5. One main reason may be that SSEGCN can learn structure information from local to global when the syntactic distance becomes larger and SSEGCN achieves remarkable results on five syntactic mask matrices. However, increasing the number of syntactic
Aspect	Sentence	ATAE-LSTM	IAN	CDT	SSEGCN	Label
{food, service}	Great food but the service was dreadful!	(N, N)	(N, N)	(P, N)	(P, N)	(P, N)
{Windows 8}	Biggest complaint is Windows 8.	(O)	(O)	(N)	(N)	(N)
{settings}	The settings are not user-friendly either.	(N)	(P)	(N)	(N)	(N)
{i5}	Not as fact as I would have expect for an i5	(P)	(P)	(O)	(N)	(N)
{Tequila}	If you are a Tequila fan you will not be disappointed.	(N)	(N)	(N)	(P)	(P)
Table 4: Case study of our SSEGCN model compared with state-of-the-art baselines.
(a) The attention score of self-attention.
(b) The attention score of aspect-aware attention.
(c) The attention score of attention layer with syntax-mask layer.
Figure 3: Two visualized examples on how aspect-aware attention and syntactic mask matrix contribute to the attention layer.
Figure 4: Effect of the number of SSEGCN layers.
Figure 5: Effect of the number of syntactic mask matrices.
mask matrices from 5 to 7 leads to the performance degradation of SSEGCN. When the syntactic distance is greater than five, multiple syntactic mask matrices are fully connected matrices, and leads to
the introduction of noise and decline of the model performance.
In this paper, we propose a SSEGCN architecture which integrates semantic information along with the syntactic structure for ABSA task. Specifically, we first design an aspect-aware attention mechanism, which is responsible for learning aspect-related semantic information. Then, we combine it with the self-attention to compose the attention layer. Furthermore, we construct syntactic mask matrices of a sentence according to the different syntactic distances that learn local to global structure information. Consequently, we combine attention score matrices with syntactic mask matrices to fuse the semantic and syntactic information. Experimental results demonstrate the effectiveness of our approach on three public datasets.
This work was supported in part by the Shandong Provincial Natural Science Foundation (No.ZR2020MF149), in part by the Science and Technology Commission of Shanghai Municipality (21511100302).
Chenhua Chen, Zhiyang Teng, and Yue Zhang. 2020. Inducing target-specific latent structures for aspect sentiment classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5596-5607.
Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 452-461.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke 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 (volume 2: Short papers), pages 49-54.
Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018. Multi-grained attention network for aspect-level sen
timent classification. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 3433-3442.
Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, and Xiaowei Shen. 2018. Can: Constrained attention networks for multi-aspect sentiment analysis. arXiv preprint arXiv:1812.10735.
Binxuan Huang and Kathleen M Carley. 2019. Syntax-aware aspect level sentiment classification with graph attention networks. arXiv preprint arXiv:1909.02606.
Binxuan Huang, Yanglan Ou, and Kathleen M Carley. 2018. Aspect level sentiment classification with attention-over-attention neural networks. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pages 197-206. Springer.
Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 151-160.
Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, and Saif Mohammad. 2014. Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pages 437-442.
Ruifan Li, Hao Chen, Fangxiang Feng, Zhanyu Ma, Xiaojie Wang, and Eduard 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 (Volume 1: Long Papers), pages 6319-6329.
Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018. Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086.
Bin Liang, Hang Su, Lin Gui, Erik Cambria, and Ruifeng Xu. 2021. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Systems, page 107643.
Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, and Ruifeng Xu. 2020. Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 150-161.
Qiao Liu, Haibin Zhang, Yifu Zeng, Ziqi Huang, and Zufeng Wu. 2018. Content attention model for aspect based sentiment analysis. In Proceedings of the 2018 World Wide Web Conference, pages 1023-1032.
Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. 2017. Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893.
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543.
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, and Suresh Manandhar. 2014. Semeval-2014 task 4: Aspect based sentiment analysis. Proceedings of International Workshop on Semantic Evaluation at.
Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, and Xudong Liu. 2019. Aspect-level sentiment analysis via convolution over dependency tree. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5679-5688.
Hao Tang, Donghong Ji, Chenliang Li, and Qiji 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, pages 6578-6588.
Yuanhe Tian, Guimin Chen, and Yan Song. 2021. Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2910-2922.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008.
Jingjing Wang, Jie Li, Shoushan Li, Yangyang Kang, Min Zhang, Luo Si, and Guodong Zhou. 2018. Aspect sentiment classification with both word-level and clause-level attention networks. In *IJCAI*, volume 2018, pages 4439-4445.
Kai Wang, Weizhou Shen, Yunyi Yang, Xiaojun Quan, and Rui Wang. 2020. Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362.
Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing, pages 606-615.
Zeguan Xiao, Jiarun Wu, Qingliang Chen, and Congjian Deng. 2021. Bert4gcn: Using bert intermediate layers to augment gcn for aspect-based sentiment classification. arXiv preprint arXiv:2110.00171.
Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477.
Mi Zhang and Tieyun Qian. 2020. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3540-3549.
Pinlong Zhao, Linlin Hou, and Ou Wu. 2020. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowledge-Based Systems, 193:105443.