{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:33:50Z","timestamp":1773776030744,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uygur Autonomous Region","award":["2022D04079"],"award-info":[{"award-number":["2022D04079"]}]},{"name":"Xinjiang Uygur Autonomous Region","award":["11504313"],"award-info":[{"award-number":["11504313"]}]},{"name":"Xinjiang Uygur Autonomous Region","award":["U1911401"],"award-info":[{"award-number":["U1911401"]}]},{"name":"Xinjiang Uygur Autonomous Region","award":["61433012"],"award-info":[{"award-number":["61433012"]}]},{"name":"the National Science Foundation of China","award":["2022D04079"],"award-info":[{"award-number":["2022D04079"]}]},{"name":"the National Science Foundation of China","award":["11504313"],"award-info":[{"award-number":["11504313"]}]},{"name":"the National Science Foundation of China","award":["U1911401"],"award-info":[{"award-number":["U1911401"]}]},{"name":"the National Science Foundation of China","award":["61433012"],"award-info":[{"award-number":["61433012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Currently, attention mechanisms are widely used in aspect-level sentiment analysis tasks. Previous studies have only used attention mechanisms combined with neural networks for aspect-level sentiment classification, and the feature extraction of the model is insufficient. When the same aspect and sentiment polarity appear in multiple sentences, the semantic information sharing of the same domain is also ignored, resulting in low model performance. To address these problems, the paper proposes an aspect-level sentiment analysis model, GCAT-GTCU, which combines a Graph-connected Attention Network containing symmetry with Gate Than Change Unit. Three nodes of words, sentences, and aspects are constructed, and local and deep-level features of sentences are extracted using CNN splicing BiGRU; node connection information is added to GAT to form a GCAT containing symmetry to realize the information interaction of three nodes, pay attention to the contextual information, and update the shared information of three nodes at any time; a new gating mechanism GTCU is constructed to filter noisy information and control the flow of sentiment information; finally, the three nodes are extracted information to predict the final sentiment polarity. The experimental results on four publicly available datasets show that the model outperforms the baseline model against which it is compared in some very controlled situations.<\/jats:p>","DOI":"10.3390\/sym15020309","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T03:26:41Z","timestamp":1674444401000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-968X","authenticated-orcid":false,"given":"Chunming","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Xiuhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Huiru","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6896-9382","authenticated-orcid":false,"given":"Ying","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/TKDE.2015.2485209","article-title":"Survey on aspect-level sentiment analysis","volume":"28","author":"Schouten","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Brun, C., Popa, D.N., and Roux, C. (2014, January 23\u201329). XRCE: Hybrid Classification for Aspect-based Sentiment Analysis. Proceedings of the COLING, Dublin, Ireland.","DOI":"10.3115\/v1\/S14-2149"},{"key":"ref_3","first-page":"71","article-title":"A fine-grained sentiment analysis of online guest reviews of economy hotels in China","volume":"30","author":"Luo","year":"2021","journal-title":"J. Hosp. Mark. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.eswa.2018.10.003","article-title":"Deep learning for aspect-based sentiment analysis: A comparative review","volume":"118","author":"Do","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_5","first-page":"743","article-title":"Fine-grained sentiment analysis for social network platform based on deep-learning model","volume":"34","author":"Li","year":"2017","journal-title":"Appl. Res. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Robinson, D., and Tepper, J. (2018, January 2). Detecting hate speech on twitter using a convolution-gru based deep neural network. Proceedings of the European Semantic Web Conference, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-319-93417-4_48"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"77820","DOI":"10.1109\/ACCESS.2020.2990306","article-title":"Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations","volume":"8","author":"Aydin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xue, W., and Li, T. (2018). Aspect based sentiment analysis with gated convolutional networks. arXiv.","DOI":"10.18653\/v1\/P18-1234"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., and Liu, T. (2015, January 26\u201331). Learning semantic representations of users and products for document level sentiment classification. Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP), Beijing, China.","DOI":"10.3115\/v1\/P15-1098"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Q., and Song, D. (2019). Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv.","DOI":"10.18653\/v1\/D19-1464"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Wang, K., Shen, W., Yang, Y., Quan, X., and Wang, R. (2020). Relational graph attention network for aspect-based sentiment analysis. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wu, G., Qian, X., and Zhang, B. (2022, January 25\u201328). Graph Attention Network for Financial Aspect-based Sentiment Classification with Contrastive Learning. Proceedings of the 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), Perth, Australia.","DOI":"10.1109\/INDIN51773.2022.9976125"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, J.B., Bao, Y., and Zheng, W.T. (2022). Analyses of Some Structural Properties on a Class of Hierarchical Scale-free Networks. arXiv.","DOI":"10.1142\/S0218348X22501365"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1142\/S0218348X21502601","article-title":"Network coherence analysis on a family of nested weighted n-polygon networks","volume":"29","author":"Liu","year":"2021","journal-title":"Fractals"},{"key":"ref_17","unstructured":"Li, Z., Mak, M.W., and Meng, H.M.L. (2022). Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Z., and Mak, M.W. (2022, January 7\u201310). Speaker Representation Learning via Contrastive Loss with Maximal Speaker Separability. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand.","DOI":"10.23919\/APSIPAASC55919.2022.9980014"},{"key":"ref_19","unstructured":"Sheng, J., Zhang, Y., Cai, J., Lam, S.K., Li, Z., Zhang, J., and Teng, X. (2022). Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3570","DOI":"10.1109\/ACCESS.2020.3048088","article-title":"Knowledge-guided sentiment analysis via learning from natural language explanations","volume":"9","author":"Ke","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"148489","DOI":"10.1109\/ACCESS.2020.3015854","article-title":"AgglutiFiT: Efficient low-resource agglutinative language model fine-tuning","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","unstructured":"Li, X., Li, Z., Sheng, J., and Slamu, W. (November, January 31). Low-Resource Text Classification via Cross-Lingual Language Model Fine-Tuning. Proceedings of the China National Conference on Chinese Computational Linguistics, Haikou, Hainan, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hsu, T.W., Chen, C.C., Huang, H.H., and Chen, H.H. (2021, January 7\u201311). Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online.","DOI":"10.18653\/v1\/2021.emnlp-main.362"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, K., Zhang, M., Zhao, H., Liu, Q., Wu, W., and Chen, E. (2022). Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis. arXiv.","DOI":"10.18653\/v1\/2022.findings-acl.285"},{"key":"ref_25","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, Seattle, WA, USA.","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Liu, W., and Xiong, C. (2022). A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis. arXiv.","DOI":"10.18653\/v1\/2022.findings-naacl.58"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zheng, J., Friedman, S., Schmer-Galunder, S., Magnusson, I., Wheelock, R., Gottlieb, J., and Miller, C. (2022, January 14). Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), Online.","DOI":"10.18653\/v1\/2022.woah-1.19"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, G., and Song, Y. (2021, January 6\u201311). Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Mexico City, Mexico.","DOI":"10.18653\/v1\/2021.naacl-main.231"},{"key":"ref_29","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_31","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_32","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., and Grangier, D. (2017, January 6\u201311). Language modeling with gated convolutional networks. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_33","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. Inf. Technol. (AJCSIT)"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., L-Smadi, M.A., Al-Ayyoub, M., Zhao, Y., Qin, B., and Clercq, O.D. (2016, January 16\u201317). Semeval-2016 task 5: Aspect based sentiment analysis. Proceedings of the International Workshop on Semantic Evaluation, San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1002"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional neural networks for sentence classification. Proceedings of the Conference Empirical Methods Natural Lang, Process (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_38","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_39","unstructured":"Sun, C., Huang, L., and Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, Y., Yin, C., and Zhong, S. (2020, January 16\u201318). Sentence constituent-aware aspect-category sentiment analysis with graph attention networks. Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing, Zhengzhou, Henan, China.","DOI":"10.1007\/978-3-030-60450-9_64"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Chen, L., Xu, R., Ao, X., and Yang, M. (2019, January 5\u20137). A challenge dataset and effective models for aspect-based sentiment analysis. 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\/D19-1654"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, A., Huang, M., and Zhu, X. (2019, January 13\u201317). Aspect-level sentiment analysis using as-capsules. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313750"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:13:14Z","timestamp":1760119994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,22]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["sym15020309"],"URL":"https:\/\/doi.org\/10.3390\/sym15020309","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,22]]}}}