AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task

Research Article
Open access

AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task

Haocheng Xi 1* , Yutong Wang 2 , Xinyu Wang 3 , Zhitao Li 4 , Bochun Liu 5 , Yihan Wang 6 , Chen Ni 7
  • 1 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • 2 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • 3 National Pilot School of software, Yunnan University, Yunnan 650500, China    
  • 4 School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China    
  • 5 National Pilot School of software, Yunnan University, Yunnan 650500, China    
  • 6 National Pilot School of software, Yunnan University, Yunnan 650500, China    
  • 7 School of Information Science and Engineering, Yunnan University, Yunnan 650500, China    
  • *corresponding author shaunspike813jelly@gmail.com
Published on 8 November 2024 | https://doi.org/10.54254/2755-2721/101/20240999
ACE Vol.101
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-691-4
ISBN (Online): 978-1-83558-692-1

Abstract

This study presents the AWA-GCN (Attention-Weighted Affine with Graph Convolutional Networks) model, a new method designed exclusively for the SIGHAN 2024 Chinese Dimensional Aspect-Based Sentiment Analysis (dimABSA) Task. This test requires participants to identify sentiment triples and sentiment quadruples from textual data. The data comprises aspects, sentiment views, categories, and intensities across valence-arousal dimensions. Our model differs from existing models by utilizing a complex multi-layer attention mechanism within a GCN architecture, instead of relying on pipeline techniques or Grid Tagging Schemes (GTS). This design successfully captures the intricate interconnections among many sentiment components. The AWA-GCN model is very innovative in its ability to effectively handle quadruple extraction, which is a complex problem that has not been handled by traditional methods. Our approach enhances the understanding of aspect-sentiment interactions by including sophisticated techniques like word-level attention and semantic graph representations. The AWA- GCN model outperforms previous baselines in precision, recall, and F1 score on the dimABSA dataset, as demonstrated by empirical assessments. Additionally, the model exhibits significant enhancements in capturing the dimensional features of sentiment expressions. The results validate the model’s exceptional ability to handle the complex nuances necessary for successful sentiment analysis in simple Chinese.

Keywords:

Graph Convolutional Network(GCN), Chinese Sentiment Analysis, Sentiment Triple Extraction, Sentiment Quadruple Extraction, Valence-Arousal Dimensions.

Xi,H.;Wang,Y.;Wang,X.;Li,Z.;Liu,B.;Wang,Y.;Ni,C. (2024). AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task. Applied and Computational Engineering,101,87-110.
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References

[1]. Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammed AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphe ́e De Clercq, et al. Semeval-2016 task 5: Aspect based sentiment analysis. In ProWorkshop on Semantic Evaluation (SemEval-2016), pages 19–30. Association for Computational Linguistics, 2016.

[2]. Caroline Brun and Vassilina Nikoulina. Aspect based sentiment analysis into the wild. In Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 116–122, 2018.

[3]. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, and Rui Xia. Grid tagging scheme for aspect-oriented fine-grained opinion extraction. arXiv preprint arXiv:2010.04640, 2020.

[4]. Bo Pang, Lillian Lee, et al. Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2):1–135, 2008.

[5]. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177, 2004.

[6]. Lu Xu, Hao Li, Wei Lu, and Lidong Bing. Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609, 2020.

[7]. Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, and Luo Si. Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8600–8607, 2020.

[8]. Lianzhe Huang, Peiyi Wang, Sujian Li, Tianyu Liu, Xiaodong Zhang, Zhicong Cheng, Dawei Yin, and Houfeng Wang. First target and opinion then polarity: Enhancing target-opinion correlation for aspect sentiment triplet extraction. arXiv preprint arXiv:2102.08549, 2021.

[9]. Hongjiang Jing, Zuchao Li, Hai Zhao, and Shu Jiang. Seeking common but distinguishing difference, a joint aspect-based sentiment analysis model. arXiv preprint arXiv:2111.09634, 2021.

[10]. Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, and Hai Jin. Semantic and syntactic enhanced aspect sentiment triplet extraction. arXiv preprint arXiv:2106.03315, 2021.

[11]. Hai Wan, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, and Jeff Z Pan. Target-aspect-sentiment joint detection for aspect-based sentiment analysis. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 9122–9129, 2020.

[12]. Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, and Shiwan Zhao. Improving aspect sentiment quad prediction via template-order data augmentation. arXiv preprint arXiv:2210.10291, 2022.

[13]. Hua Zhang, Xiawen Song, Xiaohui Jia, Cheng Yang, Zeqi Chen, Bi Chen, Bo Jiang, Ye Wang, and Rui Feng. Query-induced multi-task decomposition and enhanced learning for aspect-based sentiment quadruple prediction. Engineering Applications of Artificial Intelligence, 133:108609, 2024.

[14]. Shen Zhou and Tieyun Qian. On the strength of sequence labeling and generative models for aspect sentiment triplet extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12038–12050, 2023.

[15]. Li Yuan, Jin Wang, Liang-Chih Yu, and Xuejie Zhang. Encoding syntactic information into transformers for aspect-based sentiment triplet extraction. IEEE Transactions on Affective Computing, 2023.

[16]. Chen Zhang, Qiuchi Li, Dawei Song, and Benyou Wang. A multi-task learning framework for opinion triplet extraction. arXiv preprint arXiv:2010.01512, 2020.

[17]. Xuefeng Shi, Min Hu, Jiawen Deng, Fuji Ren, Piao Shi, and Jiaoyun Yang. Integration of multi- branch gcns enhancing aspect sentiment triplet extraction. Applied Sciences, 13(7):4345, 2023.

[18]. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.

[19]. Chi Sun, Luyao Huang, and Xipeng Qiu. Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588, 2019.

[20]. Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, and Wai Lam. Aspect sentiment quad prediction as paraphrase generation. arXiv preprint arXiv:2110.00796, 2021.

[21]. Hongjie Cai, Rui Xia, and Jianfei Yu. Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. 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 340–350, 2021.

[22]. Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, and Qinying Gu. Rethinking aste: A minimalist tagging scheme alongside contrastive learning. arXiv preprint arXiv:2403.07342, 2024.

[23]. Zhaoyang Niu, Guoqiang Zhong, and Hui Yu. A review on the attention mechanism of deep learning. Neurocomputing, 452:48–62, 2021.

[24]. Dat Quoc Nguyen and Karin Verspoor. End-to-end neural relation extraction using deep biaffine attention. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pages 729–738. Springer, 2019.

[25]. Seung-Hoon Na, Jinwoon Min, Kwanghyeon Park, Jong-Hun Shin, and Young-Gil Kim. Jbnu at mrp 2019: Multi-level biaffine attention for semantic dependency parsing. In Proceedings of the shared task on cross-framework meaning representation parsing at the 2019 conference on natural language learning, pages 95–103, 2019.

[26]. Long-ShouGAOandNa-NaLI.Aspectsentimenttripletextractionbasedonaspect-awareattention enhancement. Journal of Computer Applications, page 0, 2023.

[27]. Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.

[28]. Jin-Seong Kim, Sung-Wook Park, Jun-Yeong Kim, Jun Park, Jun-Ho Huh, Se-Hoon Jung, and Chun-Bo Sim. E-hrnet: Enhanced semantic segmentation using squeeze and excitation. Electronics, 12(17):3619, 2023.

[29]. Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 257–266, 2019.

[30]. PinlongZhao,LinlinHou,andOuWu.Modelingsentimentdependencieswithgraphconvolutional networks for aspect-level sentiment classification. Knowledge-Based Systems, 193:105443, 2020.

[31]. Jincheng Mei, Chenjun Xiao, Csaba Szepesvari, and Dale Schuurmans. On the global convergence rates of softmax policy gradient methods. In International conference on machine learning, pages 6820–6829. PMLR, 2020.

[32]. Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2974–2985, 2022.

[33]. Makoto Miwa and Yutaka Sasaki. Modeling joint entity and relation extraction with table representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1858–1869, 2014.

Cite this article

Xi,H.;Wang,Y.;Wang,X.;Li,Z.;Liu,B.;Wang,Y.;Ni,C. (2024). AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task. Applied and Computational Engineering,101,87-110.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

ISBN：978-1-83558-691-4(Print) / 978-1-83558-692-1(Online)
Editor：Mustafa ISTANBULLU
Conference website: https://2024.confmla.org/
Conference date: 12 January 2025
Series: Applied and Computational Engineering
Volume number: Vol.101
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammed AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphe ́e De Clercq, et al. Semeval-2016 task 5: Aspect based sentiment analysis. In ProWorkshop on Semantic Evaluation (SemEval-2016), pages 19–30. Association for Computational Linguistics, 2016.

[2]. Caroline Brun and Vassilina Nikoulina. Aspect based sentiment analysis into the wild. In Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 116–122, 2018.

[3]. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, and Rui Xia. Grid tagging scheme for aspect-oriented fine-grained opinion extraction. arXiv preprint arXiv:2010.04640, 2020.

[4]. Bo Pang, Lillian Lee, et al. Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2):1–135, 2008.

[5]. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177, 2004.

[6]. Lu Xu, Hao Li, Wei Lu, and Lidong Bing. Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609, 2020.

[7]. Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, and Luo Si. Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8600–8607, 2020.

[8]. Lianzhe Huang, Peiyi Wang, Sujian Li, Tianyu Liu, Xiaodong Zhang, Zhicong Cheng, Dawei Yin, and Houfeng Wang. First target and opinion then polarity: Enhancing target-opinion correlation for aspect sentiment triplet extraction. arXiv preprint arXiv:2102.08549, 2021.

[9]. Hongjiang Jing, Zuchao Li, Hai Zhao, and Shu Jiang. Seeking common but distinguishing difference, a joint aspect-based sentiment analysis model. arXiv preprint arXiv:2111.09634, 2021.

[10]. Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, and Hai Jin. Semantic and syntactic enhanced aspect sentiment triplet extraction. arXiv preprint arXiv:2106.03315, 2021.

[11]. Hai Wan, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, and Jeff Z Pan. Target-aspect-sentiment joint detection for aspect-based sentiment analysis. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 9122–9129, 2020.

[12]. Mengting Hu, Yike Wu, Hang Gao, Yinhao Bai, and Shiwan Zhao. Improving aspect sentiment quad prediction via template-order data augmentation. arXiv preprint arXiv:2210.10291, 2022.

[13]. Hua Zhang, Xiawen Song, Xiaohui Jia, Cheng Yang, Zeqi Chen, Bi Chen, Bo Jiang, Ye Wang, and Rui Feng. Query-induced multi-task decomposition and enhanced learning for aspect-based sentiment quadruple prediction. Engineering Applications of Artificial Intelligence, 133:108609, 2024.

[14]. Shen Zhou and Tieyun Qian. On the strength of sequence labeling and generative models for aspect sentiment triplet extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12038–12050, 2023.

[15]. Li Yuan, Jin Wang, Liang-Chih Yu, and Xuejie Zhang. Encoding syntactic information into transformers for aspect-based sentiment triplet extraction. IEEE Transactions on Affective Computing, 2023.

[16]. Chen Zhang, Qiuchi Li, Dawei Song, and Benyou Wang. A multi-task learning framework for opinion triplet extraction. arXiv preprint arXiv:2010.01512, 2020.

[17]. Xuefeng Shi, Min Hu, Jiawen Deng, Fuji Ren, Piao Shi, and Jiaoyun Yang. Integration of multi- branch gcns enhancing aspect sentiment triplet extraction. Applied Sciences, 13(7):4345, 2023.

[18]. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.

[19]. Chi Sun, Luyao Huang, and Xipeng Qiu. Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588, 2019.

[20]. Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, and Wai Lam. Aspect sentiment quad prediction as paraphrase generation. arXiv preprint arXiv:2110.00796, 2021.

[21]. Hongjie Cai, Rui Xia, and Jianfei Yu. Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. 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 340–350, 2021.

[22]. Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, and Qinying Gu. Rethinking aste: A minimalist tagging scheme alongside contrastive learning. arXiv preprint arXiv:2403.07342, 2024.

[23]. Zhaoyang Niu, Guoqiang Zhong, and Hui Yu. A review on the attention mechanism of deep learning. Neurocomputing, 452:48–62, 2021.

[24]. Dat Quoc Nguyen and Karin Verspoor. End-to-end neural relation extraction using deep biaffine attention. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pages 729–738. Springer, 2019.

[25]. Seung-Hoon Na, Jinwoon Min, Kwanghyeon Park, Jong-Hun Shin, and Young-Gil Kim. Jbnu at mrp 2019: Multi-level biaffine attention for semantic dependency parsing. In Proceedings of the shared task on cross-framework meaning representation parsing at the 2019 conference on natural language learning, pages 95–103, 2019.

[26]. Long-ShouGAOandNa-NaLI.Aspectsentimenttripletextractionbasedonaspect-awareattention enhancement. Journal of Computer Applications, page 0, 2023.

[27]. Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.

[28]. Jin-Seong Kim, Sung-Wook Park, Jun-Yeong Kim, Jun Park, Jun-Ho Huh, Se-Hoon Jung, and Chun-Bo Sim. E-hrnet: Enhanced semantic segmentation using squeeze and excitation. Electronics, 12(17):3619, 2023.

[29]. Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 257–266, 2019.

[30]. PinlongZhao,LinlinHou,andOuWu.Modelingsentimentdependencieswithgraphconvolutional networks for aspect-level sentiment classification. Knowledge-Based Systems, 193:105443, 2020.

[31]. Jincheng Mei, Chenjun Xiao, Csaba Szepesvari, and Dale Schuurmans. On the global convergence rates of softmax policy gradient methods. In International conference on machine learning, pages 6820–6829. PMLR, 2020.

[32]. Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2974–2985, 2022.

[33]. Makoto Miwa and Yutaka Sasaki. Modeling joint entity and relation extraction with table representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1858–1869, 2014.

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