{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:32:32Z","timestamp":1783161152881,"version":"3.54.6"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This work addresses the broken symmetry in syntactic\u2013semantic representations for Aspect-Based Sentiment Analysis, where advancements have been driven by the use of pre-trained language models to achieve contextual understanding and graph neural networks capturing aspect\u2013opinion dependencies using syntactic trees. However, long-distance aspect\u2013opinion pairs pose challenges: the structural noise in dependency trees often causes erroneous associations, while the discrete structure of the constituent trees leads to constituent fragmentation. In this paper, we propose DySynGAT and introduce a Localized Graph Attention Network (LGAT) to fuse bi-gram syntactic and semantic information from both dependency and constituent trees, effectively mitigating interference from dependency tree noise. A dynamic semantic enhancement module efficiently integrates local and global semantics, alleviating constituent fragmentation caused by constituent trees. An aspect\u2013context interaction graph (ACIG), built upon minimal semantic segmentation and jointly enhanced features, filters out noisy cross-clause edges. Spatial reduction attention (SRA) with mean pooling compresses the redundant sequential features, reducing the noise under long-range dependencies. Experiments on foods and beverages, electronics, and user review datasets demonstrate F1 score improvements of 0.55%, 3.55%, and 1.75% over SAGAT-BERT, demonstrating strong cross-domain robustness.<\/jats:p>","DOI":"10.3390\/sym17091492","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T08:40:15Z","timestamp":1757407215000},"page":"1492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-Tree-Guided Aspect-Based Sentiment Analysis Incorporating Structure-Aware Semantic Refinement and Graph Attention"],"prefix":"10.3390","volume":"17","author":[{"given":"Xinyu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lv","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhong","sequence":"additional","affiliation":[{"name":"China Xiongan Group, Xiong\u2019an 071700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peisen","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6241-1237","authenticated-orcid":false,"given":"Zhaobin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","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_2","first-page":"615","article-title":"PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer","volume":"Volume 12347","author":"Li","year":"2020","journal-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV)"},{"key":"ref_3","unstructured":"Zhao, T., Du, J., Shao, Y., and Li, A. (2023, January 22\u201324). Aspect-Based Sentiment Analysis Using Local Context Focus Mechanism with DeBERTa. Proceedings of the IEEE Internationl Conference on Web Services (ICWS), Tianjin, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, G., and Yan, S. (2021, January 6\u201311). Aspect-Based Sentiment Analysis with Type-Aware Graph Convolutional Networks and Layer Ensemble. Proceedings of the North American Chapter of the Association for Computational Linguistics, Mexico City, Mexico.","DOI":"10.18653\/v1\/2021.naacl-main.231"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJDWM.319803","article-title":"Fusing Syntax and Semantics-Based Graph Convolutional Network for Aspect-Based Sentiment Analysis","volume":"19","author":"Feng","year":"2023","journal-title":"Int. J. Data Warehous. Min."},{"key":"ref_6","unstructured":"Wang, P., Zhang, S., Li, Z., and Hou, J. (2023, January 9\u201314). Enhancing Ancient Chinese Understanding with Derived Noisy Syntax Trees. Proceedings of the ACL 2023 Short Research Workshops (ACL-SRW), Toronto, CA, USA."},{"key":"ref_7","first-page":"63","article-title":"Rhetorical relations of contrast in the blog text: Marking means, semantics, functions","volume":"16","author":"Kovtunenko","year":"2018","journal-title":"Rudn. J. Russ. Foreign Lang. Res. Teach."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ACCESS.2020.3046253","article-title":"A Residual BiLSTM Model for Named Entity Recognition","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2478\/ijanmc-2024-0019","article-title":"Digital Camouflage Generation Based on an Improved CycleGAN Network Model","volume":"9","author":"Xia","year":"2024","journal-title":"Int. J. Adv. Netw. Monit. Control."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, D., Liu, M., and Jia, J. (2023). CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15184455"},{"key":"ref_11","unstructured":"Zhou, J., Zhang, S., and Zhao, H. (2019). Concurrent Parsing of Constituency and Dependency. arXiv."},{"key":"ref_12","unstructured":"Jaime, J.A.S., Robert, R.W., Tom, L., and Scott, B. (2010, January 14\u201317). Fracture Mapping Using Offset Vector Tile Technology. Proceedings of the IV Simp\u00f3sio Brasileiro de Geof\u00edsica (4SIMBGF), Brasilia, Brazil."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yao, G., Zhu, S., Zhang, L., and Qi, M. (2024). HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images. Sensors, 24.","DOI":"10.20944\/preprints202406.1963.v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Gao, X., Zhang, W., Liu, S., and Zhang, Y. (2018). A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification. Future Internet, 10.","DOI":"10.3390\/fi10120116"},{"key":"ref_15","first-page":"2879","article-title":"Convolution neural network feature importance analysis and feature selection enhanced model","volume":"28","author":"Lu","year":"2017","journal-title":"J. Softw."},{"key":"ref_16","unstructured":"Sun, C., Huang, L., and Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv."},{"key":"ref_17","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, Seattle, WA, USA.","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cui, Z., Park, H.J., and Noh, G. (2022). BHGAttN: A Feature-Enhanced Hierarchical Graph Attention Network for Sentiment Analysis. Entropy, 24.","DOI":"10.3390\/e24111691"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chang, B., Lee, I., Kim, H., and Kang, J. (2021). \u201cKilling Me\u201d Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism. arXiv.","DOI":"10.18653\/v1\/2021.eacl-main.315"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., and Shirai, K. (2015, January 17\u201321). Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1298"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, K., Zhang, R., Mensah, S., Mao, Y., and Li, 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 (EMNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1569"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, S., Wang, W., and Deng, L. (2022). RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks. Appl. Sci., 12.","DOI":"10.3390\/app122312108"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., and De Clercq, O. (2016, January 16\u201317). SemEval-2016 Task 5: Aspect Based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1002"},{"key":"ref_25","first-page":"55","article-title":"Adaptive Recursive Neural Network for Target-Dependent Twitter Sentiment Classification","volume":"Volume 2","author":"Li","year":"2014","journal-title":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Chen, L., Xu, R., Ao, X., and Yang, M. (2019, January 3\u20137). A challenge dataset and effective models for aspect-based sentiment analysis. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1654"},{"key":"ref_27","unstructured":"Tane, D., Qin, B., Feng, X., and Liu, T. (2015). Effective LSTMs for Target-Dependent Sentiment Classification. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., and Liu, T. (2016). Aspect level sentiment classification with deep memory network. arXiv.","DOI":"10.18653\/v1\/D16-1021"},{"key":"ref_29","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, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/568"},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","unstructured":"Huang, B., and Carley, K.M. (2019). Syntax-aware aspect level sentiment classification with graph attention networks. arXiv.","DOI":"10.18653\/v1\/D19-1549"},{"key":"ref_33","unstructured":"Xu, H., Liu, B., Shu, L., and Yu, P.S. (2019). BERT post-training for review reading comprehension and aspect-based sentiment analysis. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, L., Sun, X., Li, S., Zhang, L., and Wang, H. (2020, January 8\u201313). Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification. Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.69"},{"key":"ref_35","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 (ACL 2021), Bangkok, Thailand.","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"ref_36","first-page":"110025","article-title":"Integrating External Knowledge into Aspect-Based Sentiment Analysis Using Graph Neural Network","volume":"256","author":"Gu","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125890","DOI":"10.1016\/j.eswa.2024.125890","article-title":"FITE-GAT: Enhancing aspect-level sentiment classification with FT-RoBERTa induced trees and graph attention network","volume":"264","author":"Fan","year":"2024","journal-title":"Expert Syst. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/9\/1492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:42:22Z","timestamp":1760035342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/9\/1492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["sym17091492"],"URL":"https:\/\/doi.org\/10.3390\/sym17091492","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]}}}