{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T09:41:03Z","timestamp":1777023663790,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s13042-026-03089-2","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:46:27Z","timestamp":1777020387000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SGFNet: semantic-guided fusion network with AMR and dependency trees for aspect-based sentiment analysis"],"prefix":"10.1007","volume":"17","author":[{"given":"Xiaopeng","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailong","family":"Ning","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhuo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"issue":"19","key":"3089_CR1","doi-asserted-by":"publisher","first-page":"56619","DOI":"10.1007\/s11042-023-17701-y","volume":"83","author":"AK Zarandi","year":"2024","unstructured":"Zarandi AK, Mirzaei S (2024) A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods. Multimedia Tools Appl 83(19):56619\u201356695","journal-title":"Multimedia Tools Appl"},{"key":"3089_CR2","doi-asserted-by":"crossref","unstructured":"Li E, Li T, Liang T, Kang A, Chen K, Luo H (2025) Cross-lingual sentiment analysis empowered by emotional mutual reinforcement through emojis. Int J Mach Learn Cybern, pp 1\u201315","DOI":"10.1007\/s13042-025-02610-3"},{"key":"3089_CR3","unstructured":"Lin B, Lin Z, Li F, Liang Z, Lu Z, Xue Y (2024) Knowledge-aware interaction networks for domain-adaptive end-to-end aspect-based sentiment analysis. Int J Mach Learn Cybern, pp 1\u201315"},{"key":"3089_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111724","volume":"293","author":"J Yang","year":"2024","unstructured":"Yang J, Xiao Y, Du X (2024) Multi-grained fusion network with self-distillation for aspect-based multimodal sentiment analysis. Knowl-Based Syst 293:111724","journal-title":"Knowl-Based Syst"},{"issue":"12","key":"3089_CR5","doi-asserted-by":"publisher","first-page":"6027","DOI":"10.1007\/s13042-024-02299-w","volume":"15","author":"J Pei","year":"2024","unstructured":"Pei J, Zhang Z-L, Liu W-A (2024) Sentiment classification of movie reviews: a powerful method based on ensemble of classifiers and features. Int J Mach Learn Cybern 15(12):6027\u20136048","journal-title":"Int J Mach Learn Cybern"},{"issue":"7","key":"3089_CR6","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1007\/s13042-023-02053-8","volume":"15","author":"J Liu","year":"2024","unstructured":"Liu J, Chen W, Wang L, Ding F (2024) A hybrid depression detection model and correlation analysis for social media based on attention mechanism. Int J Mach Learn Cybern 15(7):2631\u20132642","journal-title":"Int J Mach Learn Cybern"},{"key":"3089_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112656","volume":"305","author":"M Yu","year":"2024","unstructured":"Yu M, Peng F, Zhao Y, Zhang W, Yu J, Zhao M (2024) Idsv-gcn: integrating dual syntactic views graph convolutional network for aspect-based sentiment analysis. Knowl-Based Syst 305:112656. https:\/\/doi.org\/10.1016\/j.knosys.2024.112656","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"3089_CR8","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TKDE.2015.2485209","volume":"28","author":"K Schouten","year":"2015","unstructured":"Schouten K, Frasincar F (2015) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813\u2013830","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3089_CR9","doi-asserted-by":"crossref","unstructured":"Yang Y, Hu X, Ma F, Li S, Liu A, Wen L, Yu PS (2023) Gaussian prior reinforcement learning for nested named entity recognition. In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10097163"},{"key":"3089_CR10","doi-asserted-by":"crossref","unstructured":"Li S, Hu X, Lin L, Wen L (2022) Pair-level supervised contrastive learning for natural language inference. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 8237\u20138241. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746499"},{"key":"3089_CR11","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 452\u2013461","DOI":"10.18653\/v1\/D17-1047"},{"key":"3089_CR12","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"3089_CR13","doi-asserted-by":"crossref","unstructured":"Du C, Sun H, Wang J, Qi Q, Liao J, Xu T, Liu M (2019) Capsule network with interactive attention for aspect-level sentiment classification. 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), pp 5489\u20135498","DOI":"10.18653\/v1\/D19-1551"},{"key":"3089_CR14","unstructured":"Gu S, Zhang L, Hou Y, Song Y (2018) A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th international conference on computational linguistics, pp 774\u2013784"},{"key":"3089_CR15","doi-asserted-by":"crossref","unstructured":"Xiao Z, Wu J, Chen Q, Deng C, Bert4gcn: Using bert intermediate layers to augment gcn for aspect-based sentiment classification. arxiv 2021. arXiv preprint arXiv:2110.00171","DOI":"10.18653\/v1\/2021.emnlp-main.724"},{"key":"3089_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109409","volume":"252","author":"EF Ayetiran","year":"2022","unstructured":"Ayetiran EF (2022) Attention-based aspect sentiment classification using enhanced learning through cnn-bilstm networks. Knowl-Based Syst 252:109409. https:\/\/doi.org\/10.1016\/j.knosys.2022.109409","journal-title":"Knowl-Based Syst"},{"key":"3089_CR17","doi-asserted-by":"crossref","unstructured":"Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477","DOI":"10.18653\/v1\/D19-1464"},{"key":"3089_CR18","doi-asserted-by":"crossref","unstructured":"Tang H, Ji D, Li C, Zhou Q (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, pp 6578\u20136588","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"3089_CR19","doi-asserted-by":"crossref","unstructured":"Li R, Chen H, Feng F, Ma Z, Wang X, Hovy E (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), pp 6319\u20136329","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"3089_CR20","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhou Z, Wang Y (2022) Ssegcn: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 2022 conference of the North American Chapter of the Association for Computational linguistics: human language technologies, pp 4916\u20134925","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"3089_CR21","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893","DOI":"10.24963\/ijcai.2017\/568"},{"key":"3089_CR22","doi-asserted-by":"crossref","unstructured":"Wang J, Li J, Li S, Kang Y, Zhang M, Si L, Zhou G (2018) Aspect sentiment classification with both word-level and clause-level attention networks. In: IJCAI, vol. 2018, pp 4439\u20134445","DOI":"10.24963\/ijcai.2018\/617"},{"key":"3089_CR23","doi-asserted-by":"publisher","unstructured":"Li L, Liu Y, Zhou A (2018) Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Korhonen A, Titov I (eds) Proceedings of the 22nd conference on computational natural language learning, pp 181\u2013189. Association for Computational Linguistics, Brussels, Belgium. https:\/\/doi.org\/10.18653\/v1\/K18-1018","DOI":"10.18653\/v1\/K18-1018"},{"key":"3089_CR24","doi-asserted-by":"publisher","unstructured":"Hu M, Zhao S, Zhang L, Cai K, Su Z, Cheng R, Shen X (2019) CAN: Constrained attention networks for multi-aspect sentiment analysis. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 4601\u20134610. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1467","DOI":"10.18653\/v1\/D19-1467"},{"key":"3089_CR25","doi-asserted-by":"publisher","unstructured":"Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433\u20133442. Association for Computational Linguistics, Brussels, Belgium . https:\/\/doi.org\/10.18653\/v1\/D18-1380","DOI":"10.18653\/v1\/D18-1380"},{"key":"3089_CR26","doi-asserted-by":"crossref","unstructured":"Tan X, Cai Y, Zhu C (2019) Recognizing conflict opinions in aspect-level sentiment classification with dual attention networks. In: Conference on empirical methods in natural language processing. https:\/\/api.semanticscholar.org\/CorpusID:202782487","DOI":"10.18653\/v1\/D19-1342"},{"key":"3089_CR27","doi-asserted-by":"crossref","unstructured":"Sun K, Zhang R, Mensah S, Mao Y, Liu X (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), pp 5679\u20135688","DOI":"10.18653\/v1\/D19-1569"},{"key":"3089_CR28","doi-asserted-by":"crossref","unstructured":"Liang B, Yin R, Gui L, Du J, Xu R (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, pp 150\u2013161","DOI":"10.18653\/v1\/2020.coling-main.13"},{"key":"3089_CR29","doi-asserted-by":"publisher","unstructured":"Zhang M, Qian T (2020) Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 3540\u20133549. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.286","DOI":"10.18653\/v1\/2020.emnlp-main.286"},{"key":"3089_CR30","doi-asserted-by":"publisher","unstructured":"Pouran Ben\u00a0Veyseh A, Nouri N, Dernoncourt F, Tran QH, Dou D, Nguyen TH (2020) Improving aspect-based sentiment analysis with gated graph convolutional networks and syntax-based regulation. In: Cohn T, He Y, Liu Y (eds) Findings of the association for computational linguistics: EMNLP, pp 4543\u20134548. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.407","DOI":"10.18653\/v1\/2020.findings-emnlp.407"},{"key":"3089_CR31","doi-asserted-by":"crossref","unstructured":"Zhao X, Peng H, Dai Q, Bai X, Peng H, Liu Y, Guo Q, Yu PS (2024) Rdgcn: reinforced dependency graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 17th ACM international conference on web search and data mining, pp 976\u2013984","DOI":"10.1145\/3616855.3635775"},{"key":"3089_CR32","doi-asserted-by":"publisher","unstructured":"Chen B, Ouyang Q, Luo Y, Xu B, Cai R, Hao Z (2024) S$$^2$$GSL: incorporating segment to syntactic enhanced graph structure learning for aspect-based sentiment analysis. In: Ku, L-W, Martins A, Srikumar V (eds) Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 13366\u201313379. Association for Computational Linguistics, Bangkok, Thailand. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.721","DOI":"10.18653\/v1\/2024.acl-long.721"},{"key":"3089_CR33","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhang B, Yang R, Guo C, Li M (2024) DAGCN: distance-based and aspect-oriented graph convolutional network for aspect-based sentiment analysis. In: Duh K, Gomez H, Bethard S (eds) Findings of the association for computational linguistics: NAACL 2024, pp 1863\u20131876. Association for Computational Linguistics, Mexico City, Mexico. https:\/\/doi.org\/10.18653\/v1\/2024.findings-naacl.120","DOI":"10.18653\/v1\/2024.findings-naacl.120"},{"key":"3089_CR34","doi-asserted-by":"publisher","unstructured":"Zhang F, Zheng W, Yang Y (2024) Graph convolutional network with syntactic dependency for aspect-based sentiment analysis. Int J Comput Intell Syst 17(37). https:\/\/doi.org\/10.1007\/s44196-024-00419-6","DOI":"10.1007\/s44196-024-00419-6"},{"key":"3089_CR35","doi-asserted-by":"crossref","unstructured":"Huang X, Peng H, Sun S, Hao Z, Lin H, Wang S (2025) Multi-view attention syntactic enhanced graph convolutional network for aspect-based sentiment analysis. arXiv preprint arXiv:2501.15968","DOI":"10.1007\/978-981-95-3830-0_19"},{"issue":"3","key":"3089_CR36","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3390\/electronics13030517","volume":"13","author":"X Song","year":"2024","unstructured":"Song X, Ling G, Tu W, Chen Y (2024) Knowledge-guided heterogeneous graph convolutional network for aspect-based sentiment analysis. Electronics 13(3):517. https:\/\/doi.org\/10.3390\/electronics13030517","journal-title":"Electronics"},{"key":"3089_CR37","doi-asserted-by":"publisher","unstructured":"Zong L, Hu D, Gui Q, Zhang P, Wang J, et al (2025) Jointly learning type-aware relations and inter-aspect with graph convolutional networks for aspect sentiment analysis. Neural Process Lett 57(3). https:\/\/doi.org\/10.1007\/s11063-024-11715-9","DOI":"10.1007\/s11063-024-11715-9"},{"key":"3089_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107643","volume":"235","author":"B Liang","year":"2022","unstructured":"Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl-Based Syst 235:107643. https:\/\/doi.org\/10.1016\/j.knosys.2021.107643","journal-title":"Knowl-Based Syst"},{"key":"3089_CR39","unstructured":"Zhong P, Zhang C, Wang H, Cambria E (2022) Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis. arXiv preprint arXiv:2201.04831"},{"key":"3089_CR40","doi-asserted-by":"crossref","unstructured":"Seilsepour A, Alizadeh M, Ravanmehr R, Beheshti MT, Nassiri R (2022) Self-supervised sentiment classification based on semantic similarity measures and contextual embedding using metaheuristic optimizer. In: 2022 8th Iranian conference on signal processing and intelligent systems (ICSPIS), pp 1\u20137. IEEE","DOI":"10.1109\/ICSPIS56952.2022.10043914"},{"issue":"17","key":"3089_CR41","doi-asserted-by":"publisher","first-page":"19809","DOI":"10.1007\/s11227-023-05423-9","volume":"79","author":"A Seilsepour","year":"2023","unstructured":"Seilsepour A, Ravanmehr R, Nassiri R (2023) Topic sentiment analysis based on deep neural network using document embedding technique. J Supercomput 79(17):19809\u201319847","journal-title":"J Supercomput"},{"issue":"12","key":"3089_CR42","doi-asserted-by":"publisher","first-page":"10195","DOI":"10.1007\/s11042-024-19086-y","volume":"84","author":"M Alizadeh","year":"2025","unstructured":"Alizadeh M, Seilsepour A (2025) A novel self-supervised sentiment classification approach using semantic labeling based on contextual embeddings. Multimedia Tools Appl 84(12):10195\u201310220","journal-title":"Multimedia Tools Appl"},{"key":"3089_CR43","unstructured":"Banarescu L, Bonial C, Cai S, Georgescu M, Griffitt K, Hermjakob U, Knight K, Koehn P, Palmer M, Schneider N (2013) Abstract meaning representation for sembanking. In: Proceedings of the 7th linguistic annotation workshop and interoperability with discourse, pp 178\u2013186"},{"key":"3089_CR44","doi-asserted-by":"crossref","unstructured":"Cai D, Lam W (2020) Amr parsing via graph-sequence iterative inference. arXiv preprint arXiv:2004.05572","DOI":"10.18653\/v1\/2020.acl-main.119"},{"key":"3089_CR45","doi-asserted-by":"crossref","unstructured":"Zhou J, Naseem T, Astudillo RF, Lee Y-S, Florian R, Roukos S (2021) Structure-aware fine-tuning of sequence-to-sequence transformers for transition-based amr parsing. arXiv preprint arXiv:2110.15534","DOI":"10.18653\/v1\/2021.emnlp-main.507"},{"key":"3089_CR46","first-page":"8495","volume":"34","author":"TL Hoang","year":"2021","unstructured":"Hoang TL, Picco G, Hou Y, Lee Y-S, Nguyen L, Phan D, L\u00f3pez V, Fernandez Astudillo R (2021) Ensembling graph predictions for amr parsing. Adv Neural Inf Process Syst 34:8495\u20138505","journal-title":"Adv Neural Inf Process Syst"},{"key":"3089_CR47","doi-asserted-by":"crossref","unstructured":"Ribeiro LF, Pfeiffer J, Zhang Y, Gurevych I (2021) Smelting gold and silver for improved multilingual amr-to-text generation. arXiv preprint arXiv:2109.03808","DOI":"10.18653\/v1\/2021.emnlp-main.57"},{"key":"3089_CR48","unstructured":"Kapanipathi P, Abdelaziz I, Ravishankar S, Roukos S, Gray A, Astudillo R, Chang M, Cornelio C, Dana S, Fokoue A, et al (2020) Question answering over knowledge bases by leveraging semantic parsing and neuro-symbolic reasoning. arXiv preprint arXiv:2012.01707"},{"key":"3089_CR49","doi-asserted-by":"crossref","unstructured":"Lim J, Oh D, Jang Y, Yang K, Lim H (2020) I know what you asked: graph path learning using amr for commonsense reasoning. arXiv preprint arXiv:2011.00766","DOI":"10.18653\/v1\/2020.coling-main.222"},{"key":"3089_CR50","doi-asserted-by":"publisher","unstructured":"Ma F, Hu X, Liu A, Yang Y, Li S, Yu PS, Wen L (2023) AMR-based network for aspect-based sentiment analysis. In: Rogers A, Boyd-Graber J, Okazaki N (eds) Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pp 322\u2013337. Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.19","DOI":"10.18653\/v1\/2023.acl-long.19"},{"key":"3089_CR51","doi-asserted-by":"publisher","unstructured":"Dong L, Wei F, Tan C, Tang D, Zhou M, Xu K (2014) Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: Toutanova K, Wu H (eds) Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: short papers), pp 49\u201354. Association for Computational Linguistics, Baltimore, Maryland. https:\/\/doi.org\/10.3115\/v1\/P14-2009","DOI":"10.3115\/v1\/P14-2009"},{"key":"3089_CR52","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: aspect based sentiment analysis. In: Nakov P, Zesch T (eds) Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 27\u201335. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.3115\/v1\/S14-2004","DOI":"10.3115\/v1\/S14-2004"},{"key":"3089_CR53","doi-asserted-by":"publisher","unstructured":"Jiang Q, Chen L, Xu R, Ao X, Yang M, A challenge dataset and effective models for aspect-based sentiment analysis. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 6280\u20136285. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1654","DOI":"10.18653\/v1\/D19-1654"},{"key":"3089_CR54","doi-asserted-by":"publisher","unstructured":"Chen C, Teng Z, Zhang Y (2020) Inducing target-specific latent structures for aspect sentiment classification. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 Conference on empirical methods in natural language processing (EMNLP), pp 5596\u20135607. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.451","DOI":"10.18653\/v1\/2020.emnlp-main.451"},{"key":"3089_CR55","doi-asserted-by":"publisher","unstructured":"Wang K, Shen W, Yang Y, Quan X, Wang R (2020) Relational graph attention network for aspect-based sentiment analysis. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3229\u20133238. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.295","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"3089_CR56","doi-asserted-by":"crossref","unstructured":"Tian Y, Chen G, Song Y (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, pp 2910\u20132922","DOI":"10.18653\/v1\/2021.naacl-main.231"},{"key":"3089_CR57","doi-asserted-by":"crossref","unstructured":"Chen C, Teng Z, Wang Z, Zhang Y (2022) Discrete opinion tree induction for aspect-based sentiment analysis. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), pp 2051\u20132064","DOI":"10.18653\/v1\/2022.acl-long.145"},{"key":"3089_CR58","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K, BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) 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), pp 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"3089_CR59","doi-asserted-by":"publisher","unstructured":"Zhao X, Peng H, Dai Q, Bai X, Peng H, Liu Y, Guo Q, Yu PS (2024) Rdgcn: Reinforced dependency graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 17th ACM international conference on web search and data mining. WSDM \u201924, pp. 976\u2013984. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3616855.3635775","DOI":"10.1145\/3616855.3635775"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03089-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03089-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03089-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:46:40Z","timestamp":1777020400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03089-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":59,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3089"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03089-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"29 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"276"}}