{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:21:52Z","timestamp":1779909712667,"version":"3.53.1"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4501704"],"award-info":[{"award-number":["2022YFB4501704"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["61702333"],"award-info":[{"award-number":["61702333"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62302308"],"award-info":[{"award-number":["62302308"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["21YF1432900"],"award-info":[{"award-number":["21YF1432900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2022YFB4501704"],"award-info":[{"award-number":["2022YFB4501704"]}]},{"name":"National Natural Science Foundation of China","award":["61702333"],"award-info":[{"award-number":["61702333"]}]},{"name":"National Natural Science Foundation of China","award":["62302308"],"award-info":[{"award-number":["62302308"]}]},{"name":"National Natural Science Foundation of China","award":["21YF1432900"],"award-info":[{"award-number":["21YF1432900"]}]},{"name":"Shanghai Sailing Program","award":["2022YFB4501704"],"award-info":[{"award-number":["2022YFB4501704"]}]},{"name":"Shanghai Sailing Program","award":["61702333"],"award-info":[{"award-number":["61702333"]}]},{"name":"Shanghai Sailing Program","award":["62302308"],"award-info":[{"award-number":["62302308"]}]},{"name":"Shanghai Sailing Program","award":["21YF1432900"],"award-info":[{"award-number":["21YF1432900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect-based sentiment analysis tasks. However, most existing models still contain a large amount of noisy nodes that cannot precisely capture the contextual relationships between specific aspects. Meanwhile, most studies do not consider the connections between nodes without direct dependency edges but play critical roles in determining the sentiment polarity of an aspect. To address the aforementioned limitations, we propose a Structured Dependency Tree-based Graph Convolutional Network (SDTGCN) model. Specifically, we explore construction of a structured syntactic dependency graph by incorporating positional information, sentiment commonsense knowledge, part-of-speech tags, syntactic dependency distances, etc., to assign arbitrary edge weights between nodes. This enhances the connections between aspect nodes and pivotal words while weakening irrelevant node links, enabling the model to sufficiently express sentiment dependencies between specific aspects and contextual information. We utilize part-of-speech tags and dependency distances to discover relationships between pivotal nodes without direct dependencies. Finally, we aggregate node information by fully considering their importance to obtain precise aspect representations. Experimental results on five publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches; furthermore, the accuracy and F1-score show a significant improvement on the majority of datasets, with increases of 0.74, 0.37, 0.65, and 0.79, 0.75, 1.17, respectively. This series of enhancements highlights the effective progress made by the STDGCN model in enhancing sentiment classification performance.<\/jats:p>","DOI":"10.3390\/s24020418","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7579-2004","authenticated-orcid":false,"given":"Qin","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China"},{"name":"Key Laboratory of Embedded Systems and Service Computing of Ministry of Education, Tongji University, Shanghai 201804, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuli","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1412-8182","authenticated-orcid":false,"given":"Dongdong","family":"An","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2005-2022","authenticated-orcid":false,"given":"Jie","family":"Lian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.future.2017.09.048","article-title":"Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary","volume":"81","author":"Zhang","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","unstructured":"Mubarok, M.S., Adiwijaya, A., and Aldhi, M.D. (2017). AIP Conference Proceedings, AIP Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014, January 23\u201324). Adaptive recursive neural network for target-dependent twitter sentiment classification. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-2009"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., and Liu, T. (2016, January 1\u20135). Aspect Level Sentiment Classification with Deep Memory Network. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1021"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, M., Zhu, X., and Zhao, L. (2016, January 1\u20135). Attention-based LSTM for aspect-level sentiment classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1058"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, K., Zhang, R., Mensah, S., Mao, Y., and Liu, X. (2019, January 3\u20137). Aspect-level sentiment analysis via convolution over dependency tree. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1569"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Q., and Song, D. (2019, January 3\u20137). Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1464"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., and Hovy, E. (2021, January 1\u20136). Dual graph convolutional networks for aspect-based sentiment analysis. 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), Online.","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, K., Shen, W., Yang, Y., Quan, X., and Wang, R. (2020, January 5\u201310). Relational Graph Attention Network for Aspect-based Sentiment Analysis. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.3233\/JIFS-211045","article-title":"Lexical attention and aspect-oriented graph convolutional networks for aspect-based sentiment analysis","volume":"42","author":"Li","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ins.2022.03.082","article-title":"Aggregated graph convolutional networks for aspect-based sentiment classification","volume":"600","author":"Zhao","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8333","DOI":"10.1007\/s00521-020-05287-7","article-title":"Improving aspect-level sentiment analysis with aspect extraction","volume":"34","author":"Majumder","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, P., Sun, Z., Bing, L., and Yang, W. (2017, January 7\u201311). Recurrent attention network on memory for aspect sentiment analysis. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1047"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ma, D., Li, S., Zhang, X., and Wang, H. (2017, January 19\u201325). Interactive Attention Networks for Aspect-Level Sentiment Classification. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/568"},{"key":"ref_15","unstructured":"Gu, S., Zhang, L., Hou, Y., and Song, Y. (2018, January 20\u201326). A position-aware bidirectional attention network for aspect-level sentiment analysis. Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, J., Jiang, T., Liu, Z., and Rao, Y. (2019). Attentional Encoder Network for Targeted Sentiment Classification. arXiv.","DOI":"10.1007\/978-3-030-30490-4_9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107643","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","volume":"235","author":"Liang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xue, W., and Li, T. (2018, January 15\u201320). Aspect Based Sentiment Analysis with Gated Convolutional Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1234"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109409","DOI":"10.1016\/j.knosys.2022.109409","article-title":"Attention-based aspect sentiment classification using enhanced learning through CNN-BiLSTM networks","volume":"252","author":"Ayetiran","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.inffus.2022.10.004","article-title":"Aspect-level sentiment analysis: A survey of graph convolutional network methods","volume":"91","author":"Phan","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s10462-022-10252-y","article-title":"Survey on aspect detection for aspect-based sentiment analysis","volume":"56","author":"Frasincar","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TAFFC.2020.2970399","article-title":"Issues and challenges of aspect-based sentiment analysis: A comprehensive survey","volume":"13","author":"Nazir","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6136","DOI":"10.1007\/s10489-021-02189-6","article-title":"Syntactic and semantic analysis network for aspect-level sentiment classification","volume":"51","author":"Zhang","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Phan, M.H., and Ogunbona, P.O. (2020, January 5\u201310). Modelling context and syntactical features for aspect-based sentiment analysis. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.293"},{"key":"ref_25","unstructured":"Veyseh, A.P.B., Nour, N., Dernoncourt, F., Tran, Q.H., Dou, D., and Nguyen, T.H. (2020, January 5\u201310). Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation. Proceedings of the Findings of the Association for Computational Linguistics: Online."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105443","DOI":"10.1016\/j.knosys.2019.105443","article-title":"Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification","volume":"193","author":"Zhao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4408","DOI":"10.1007\/s10489-020-02095-3","article-title":"Aspect-gated graph convolutional networks for aspect-based sentiment analysis","volume":"51","author":"Lu","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"139346","DOI":"10.1109\/ACCESS.2020.3012637","article-title":"Aspect-specific heterogeneous graph convolutional network for aspect-based sentiment classification","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106292","DOI":"10.1016\/j.knosys.2020.106292","article-title":"Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification","volume":"205","author":"Zhou","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neucom.2021.05.078","article-title":"Incorporating explicit syntactic dependency for aspect level sentiment classification","volume":"456","author":"Ke","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.ins.2021.12.127","article-title":"Convolutional attention neural network over graph structures for improving the performance of aspect-level sentiment analysis","volume":"589","author":"Phan","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109840","DOI":"10.1016\/j.knosys.2022.109840","article-title":"Sentiment interaction and multi-graph perception with graph convolutional networks for aspect-based sentiment analysis","volume":"256","author":"Lu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhou, Z., and Wang, Y. (2022, January 10\u201315). SSEGCN: Syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Online.","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liang, C., Fu, Y., and Lv, C. (2020, January 4\u20136). Structurally Enhanced Interactive Attention Network for Aspect-Level Sentiment Classification. Proceedings of the 2020 International Conference on Asian Language Processing (IALP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IALP51396.2020.9310454"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ma, Y., Peng, H., and Cambria, E. (2018, January 2\u20137). Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. Proceedings of the AAAI Conference on Artificial Intelligence, Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12048"},{"key":"ref_36","first-page":"72","article-title":"Aspect based sentiment analysis semeval-2014 task 4","volume":"4","author":"Kirange","year":"2014","journal-title":"Asian J. Comput. Sci. Technol. (AJCST)"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., and Androutsopoulos, I. (2015, January 4\u20135). Semeval-2015 task 12: Aspect based sentiment analysis. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, CO, USA.","DOI":"10.18653\/v1\/S15-2082"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Brun, C., Perez, J., and Roux, C. (2016, January 16\u201317). XRCE at SemEval-2016 task 5: Feedbacked ensemble modeling on syntactico-semantic knowledge for aspect based sentiment analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1044"},{"key":"ref_39","unstructured":"Tang, D., Qin, B., Feng, X., and Liu, T. (2015). Effective LSTMs for Target-Dependent Sentiment Classification. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fan, F., Feng, Y., and Zhao, D. (November, January 31). Multi-grained attention network for aspect-level sentiment classification. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1380"},{"key":"ref_41","unstructured":"Huang, B., Ou, Y., and Carley, K.M. (2018, January 10\u201313). Aspect level sentiment classification with attention-over-attention neural networks. Proceedings of the Social, Cultural, and Behavioral Modeling: 11th International Conference, SBP-BRiMS 2018, Washington, DC, USA. Proceedings 11."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, X., Bing, L., Lam, W., and Shi, B. (2018, January 15\u201320). Transformation Networks for Target-Oriented Sentiment Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1087"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Qian, T. (2020, January 16\u201320). Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.286"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2021.11.081","article-title":"Relation construction for aspect-level sentiment classification","volume":"586","author":"Zeng","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liang, B., Yin, R., Gui, L., Du, J., and Xu, R. (2020, January 8\u201313). Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.13"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"115712","DOI":"10.1016\/j.eswa.2021.115712","article-title":"GL-GCN: Global and local dependency guided graph convolutional networks for aspect-based sentiment classification","volume":"186","author":"Zhu","year":"2021","journal-title":"Expert Syst. 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