{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:05:07Z","timestamp":1784289907064,"version":"3.55.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62102241"],"award-info":[{"award-number":["No.62102241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["No.23ZR1425400"],"award-info":[{"award-number":["No.23ZR1425400"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s10489-024-05802-6","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T09:02:45Z","timestamp":1725008565000},"page":"11672-11689","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis"],"prefix":"10.1007","volume":"54","author":[{"given":"Haoyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4024-925X","authenticated-orcid":false,"given":"Xihe","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"issue":"1\/2","key":"5802_CR1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.aci.2019.11.003","volume":"20","author":"O Alqaryouti","year":"2024","unstructured":"Alqaryouti O et al (2024) Aspect-based sentiment analysis using smart government review data. Appl Comput Inform 20(1\/2):142\u2013161","journal-title":"Appl Comput Inform"},{"issue":"3","key":"5802_CR2","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1007\/s10462-022-10215-3","volume":"56","author":"R Bensoltane","year":"2023","unstructured":"Bensoltane R, Zaki T (2023) Aspect-based sentiment analysis: an overview in the use of arabic language. Artif Intell Rev 56(3):2325\u20132363","journal-title":"Artif Intell Rev"},{"issue":"110","key":"5802_CR3","first-page":"025","volume":"259","author":"T Gu","year":"2023","unstructured":"Gu T et al (2023) Integrating external knowledge into aspect-based sentiment analysis using graph neural network. Knowl-Based Syst 259(110):025","journal-title":"Knowl-Based Syst"},{"key":"5802_CR4","doi-asserted-by":"publisher","first-page":"107540","DOI":"10.1016\/j.knosys.2021.107540","volume":"261","author":"M Al-Smadi","year":"2023","unstructured":"Al-Smadi M et al (2023) Gated recurrent unit with multilingual universal sentence encoder for arabic aspect-based sentiment analysis. Knowl-Based Syst 261:107540","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"5802_CR5","doi-asserted-by":"publisher","first-page":"103508","DOI":"10.1016\/j.ipm.2023.103508","volume":"60","author":"L Xiao","year":"2023","unstructured":"Xiao L et al (2023) Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis. Inform Process Manag 60(6):103508","journal-title":"Inform Process Manag"},{"key":"5802_CR6","doi-asserted-by":"crossref","unstructured":"Lin T, Joe I (2023) An adaptive masked attention mechanism to act on the local text in a global context for aspect-based sentiment analysis. IEEE Access","DOI":"10.1109\/ACCESS.2023.3270927"},{"key":"5802_CR7","doi-asserted-by":"crossref","unstructured":"Huang B, et\u00a0al.(2019) Syntax-aware aspect level sentiment classification with graph attention networks. In: EMNLP. pp 5469\u20135477","DOI":"10.18653\/v1\/D19-1549"},{"key":"5802_CR8","doi-asserted-by":"crossref","unstructured":"Sun K, Zhang R, Mensah S, et\u00a0al. (2019) Aspect-level sentiment analysis via convolution over dependency tree. In: EMNLP-IJCNLP. pp 5679\u20135688","DOI":"10.18653\/v1\/D19-1569"},{"key":"5802_CR9","doi-asserted-by":"crossref","unstructured":"Zhang C, et\u00a0al. (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: EMNLP. pp 4568\u20134578","DOI":"10.18653\/v1\/D19-1464"},{"key":"5802_CR10","doi-asserted-by":"crossref","unstructured":"Tang H, Ji D, Li C, et\u00a0al. (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: ACL. pp 6578\u20136588","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"5802_CR11","doi-asserted-by":"crossref","unstructured":"Wang K, Shen W, Yang Y, et\u00a0al. (2020) Relational graph attention network for aspect-based sentiment analysis. In: ACL. pp 3229\u20133238","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"5802_CR12","doi-asserted-by":"crossref","unstructured":"Chen C, et\u00a0al. (2020) Inducing target-specific latent structures for aspect sentiment classification. In: EMNLP. pp 5596\u20135607","DOI":"10.18653\/v1\/2020.emnlp-main.451"},{"key":"5802_CR13","doi-asserted-by":"crossref","unstructured":"Li R, Zhang Y, Teng Z, et\u00a0al. (2021) Dual graph convolutional networks for aspect-based sentiment analysis. In: ACL. pp 1\u201312","DOI":"10.1109\/ICDMW53433.2021.00031"},{"key":"5802_CR14","doi-asserted-by":"crossref","unstructured":"Tan X, et\u00a0al. (2023) Self-criticism: aligning large language models with their understanding of helpfulness, honesty, and harmlessness. In: Proceedings of the 2023 conference on empirical methods in natural language processing: industry track","DOI":"10.18653\/v1\/2023.emnlp-industry.62"},{"key":"5802_CR15","doi-asserted-by":"publisher","first-page":"135499","DOI":"10.1109\/ACCESS.2020.3011802","volume":"8","author":"A Ishaq","year":"2020","unstructured":"Ishaq A, Asghar S, Gillani SA (2020) Aspect-based sentiment analysis using a hybridized approach based on cnn and ga. IEEE Access 8:135499\u2013135512","journal-title":"IEEE Access"},{"issue":"Special Issue","key":"5802_CR16","first-page":"29","volume":"12","author":"A Mohammadi","year":"2021","unstructured":"Mohammadi A, Shaverizade A (2021) Ensemble deep learning for aspect-based sentiment analysis. Int J Nonlin Anal Appl 12(Special Issue):29\u201338","journal-title":"Int J Nonlin Anal Appl"},{"key":"5802_CR17","doi-asserted-by":"publisher","first-page":"108586","DOI":"10.1016\/j.knosys.2022.108586","volume":"245","author":"G Xu","year":"2022","unstructured":"Xu G et al (2022) Aspect-level sentiment classification based on attention-bilstm model and transfer learning. Knowl-Based Syst 245:108586","journal-title":"Knowl-Based Syst"},{"issue":"7","key":"5802_CR18","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.3390\/app12073641","volume":"12","author":"BA Chandio","year":"2022","unstructured":"Chandio BA et al (2022) Attention-based ru-bilstm sentiment analysis model for roman urdu. Appl Sci 12(7):3641","journal-title":"Appl Sci"},{"key":"5802_CR19","doi-asserted-by":"publisher","first-page":"77820","DOI":"10.1109\/ACCESS.2020.2990306","volume":"8","author":"CR Aydin","year":"2020","unstructured":"Aydin CR, G\u00fcng\u00f6r T (2020) Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access 8:77820\u201377832","journal-title":"IEEE Access"},{"issue":"4","key":"5802_CR20","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.3390\/app11041366","volume":"11","author":"W Shafqat","year":"2021","unstructured":"Shafqat W, Byun YC (2021) Incorporating similarity measures to optimize graph convolutional neural networks for product recommendation. Appl Sci 11(4):1366","journal-title":"Appl Sci"},{"key":"5802_CR21","doi-asserted-by":"publisher","first-page":"189287","DOI":"10.1109\/ACCESS.2020.3031665","volume":"8","author":"N Li","year":"2020","unstructured":"Li N, Chow CY, Zhang JD (2020) Seml: a semi-supervised multi-task learning framework for aspect-based sentiment analysis. IEEE Access 8:189287\u2013189297","journal-title":"IEEE Access"},{"key":"5802_CR22","doi-asserted-by":"crossref","unstructured":"Phan MH, Ogunbona PO (2020) Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/2020.acl-main.293"},{"key":"5802_CR23","doi-asserted-by":"crossref","unstructured":"Jayanto R, Kusumaningrum R, Wibowo A (2022) Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory. Int J Adv Intell Inform 8(3)","DOI":"10.26555\/ijain.v8i3.691"},{"key":"5802_CR24","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TASLP.2020.3017093","volume":"28","author":"B Zhang","year":"2020","unstructured":"Zhang B et al (2020) Knowledge guided capsule attention network for aspect-based sentiment analysis. IEEE\/ACM Trans Audio Speech Lang Process 28:2538\u20132551","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"5802_CR25","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1007\/s10489-020-02069-5","volume":"51","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhuang T, Guo K (2021) Memory network with hierarchical multi-head attention for aspect-based sentiment analysis. Appl Intell 51:4287\u20134304","journal-title":"Appl Intell"},{"key":"5802_CR26","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.neucom.2021.05.028","volume":"454","author":"Y Liang","year":"2021","unstructured":"Liang Y et al (2021) A dependency syntactic knowledge augmented interactive architecture for end-to-end aspect-based sentiment analysis. Neurocomputing 454:291\u2013302","journal-title":"Neurocomputing"},{"issue":"2","key":"5802_CR27","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1561\/2200000096","volume":"16","author":"L Wu","year":"2023","unstructured":"Wu L et al (2023) Graph neural networks for natural language processing: a survey. Found Trends Mach Learn 16(2):119\u2013328","journal-title":"Found Trends Mach Learn"},{"key":"5802_CR28","doi-asserted-by":"crossref","unstructured":"Liu B, Wu L (2022) Graph neural networks in natural language processing. In: Graph neural networks: foundations, frontiers, and applications. p 463\u2013481","DOI":"10.1007\/978-981-16-6054-2_21"},{"key":"5802_CR29","doi-asserted-by":"crossref","unstructured":"Zhou J, et\u00a0al. (2020) Hierarchy-aware global model for hierarchical text classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/2020.acl-main.104"},{"key":"5802_CR30","doi-asserted-by":"crossref","unstructured":"Pan S et al (2024) Unifying large language models and knowledge graphs: a roadmap. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3352100"},{"issue":"111","key":"5802_CR31","first-page":"324","volume":"154","author":"X Qiu","year":"2024","unstructured":"Qiu X et al (2024) An attentive copula-based spatio-temporal graph model for multivariate time-series forecasting. Appl Soft Comput 154(111):324","journal-title":"Appl Soft Comput"},{"issue":"126","key":"5802_CR32","first-page":"441","volume":"549","author":"X Li","year":"2023","unstructured":"Li X et al (2023) A survey of graph neural network based recommendation in social networks. Neurocomputing 549(126):441","journal-title":"Neurocomputing"},{"key":"5802_CR33","doi-asserted-by":"crossref","unstructured":"Kapanipathi P, et\u00a0al. (2020) Infusing knowledge into the textual entailment task using graph convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v34i05.6318"},{"issue":"3","key":"5802_CR34","doi-asserted-by":"publisher","first-page":"bbaa110","DOI":"10.1093\/bib\/bbaa110","volume":"22","author":"H Fei","year":"2021","unstructured":"Fei H et al (2021) Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief Bioinform 22(3):bbaa110","journal-title":"Brief Bioinform"},{"key":"5802_CR35","doi-asserted-by":"publisher","first-page":"106292","DOI":"10.1016\/j.knosys.2020.106292","volume":"205","author":"J Zhou","year":"2020","unstructured":"Zhou J et al (2020) Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl-Based Syst 205:106292","journal-title":"Knowl-Based Syst"},{"key":"5802_CR36","doi-asserted-by":"publisher","first-page":"103427","DOI":"10.1016\/j.artint.2020.103427","volume":"292","author":"M Zhang","year":"2021","unstructured":"Zhang M et al (2021) Dependency-based syntax-aware word representations. Artif Intell 292:103427","journal-title":"Artif Intell"},{"key":"5802_CR37","first-page":"21687","volume":"33","author":"K Yang","year":"2020","unstructured":"Yang K, Deng J (2020) Strongly incremental constituency parsing with graph neural networks. Adv Neural Inf Process Syst 33:21687\u201321698","journal-title":"Adv Neural Inf Process Syst"},{"issue":"116","key":"5802_CR38","first-page":"513","volume":"195","author":"F Wang","year":"2022","unstructured":"Wang F et al (2022) Klgcn: knowledge graph-aware light graph convolutional network for recommender systems. Expert Syst Appl 195(116):513","journal-title":"Expert Syst Appl"},{"key":"5802_CR39","doi-asserted-by":"publisher","first-page":"110069","DOI":"10.1016\/j.knosys.2022.110069","volume":"259","author":"M Zhao","year":"2023","unstructured":"Zhao M et al (2023) Multi-task learning with graph attention networks for multi-domain task-oriented dialogue systems. Knowl-Based Syst 259:110069","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"5802_CR40","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.3233\/JIFS-201051","volume":"40","author":"W Gao","year":"2021","unstructured":"Gao W, Huang H (2021) A gating context-aware text classification model with bert and graph convolutional networks. J Intell Fuzz Sys 40(3):4331\u20134343","journal-title":"J Intell Fuzz Sys"},{"key":"5802_CR41","doi-asserted-by":"publisher","first-page":"108376","DOI":"10.1016\/j.compbiomed.2024.108376","volume":"173","author":"X Qiu","year":"2024","unstructured":"Qiu X et al (2024) Gk bertdta: a graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Comput Biol Med 173:108376","journal-title":"Comput Biol Med"},{"issue":"3","key":"5802_CR42","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/TCDS.2020.2998497","volume":"13","author":"Y Lu","year":"2020","unstructured":"Lu Y et al (2020) Cnn-g: Convolutional neural network combined with graph for image segmentation with theoretical analysis. IEEE Trans Cognit Dev Sys 13(3):631\u2013644","journal-title":"IEEE Trans Cognit Dev Sys"},{"key":"5802_CR43","doi-asserted-by":"publisher","first-page":"103219","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari C, Cannici M, Matteucci M (2021) Skeleton-based action recognition via spatial and temporal transformer networks. Comput Vis Image Underst 208:103219","journal-title":"Comput Vis Image Underst"},{"issue":"2","key":"5802_CR44","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s41019-021-00155-3","volume":"6","author":"Y Peng","year":"2021","unstructured":"Peng Y, Choi B, Xu J (2021) Graph learning for combinatorial optimization: a survey of state-of-the-art. Data Sci Eng 6(2):119\u2013141","journal-title":"Data Sci Eng"},{"key":"5802_CR45","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.ins.2020.10.030","volume":"555","author":"Q Liu","year":"2021","unstructured":"Liu Q et al (2021) Domain-specific meta-embedding with latent semantic structures. Inf Sci 555:410\u2013423","journal-title":"Inf Sci"},{"key":"5802_CR46","doi-asserted-by":"crossref","unstructured":"Duan J, et\u00a0al. (2020) A study of pre-trained language models in natural language processing. In: 2020 IEEE International Conference on Smart Cloud (SmartCloud). IEEE","DOI":"10.1109\/SmartCloud49737.2020.00030"},{"issue":"2","key":"5802_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605943","volume":"56","author":"B Min","year":"2023","unstructured":"Min B et al (2023) Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput Surv 56(2):1\u201340","journal-title":"ACM Comput Surv"},{"key":"5802_CR48","unstructured":"Hartvigsen T, et\u00a0al. (2024) Aging with grace: lifelong model editing with discrete key-value adaptors. In: Advances in neural information processing systems"},{"key":"5802_CR49","doi-asserted-by":"crossref","unstructured":"Sun W, et\u00a0al. (2023) Towards efficient and effective transformers for sequential recommendation. In: International conference on database systems for advanced applications","DOI":"10.1007\/978-3-031-30672-3_23"},{"issue":"10","key":"5802_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3561970","volume":"55","author":"N Rethmeier","year":"2023","unstructured":"Rethmeier N, Augenstein I (2023) A primer on contrastive pretraining in language processing: methods, lessons learned, and perspectives. ACM Comput Surv 55(10):1\u201317","journal-title":"ACM Comput Surv"},{"issue":"4","key":"5802_CR51","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1093\/jamia\/ocaa001","volume":"27","author":"C Lin","year":"2020","unstructured":"Lin C et al (2020) Does bert need domain adaptation for clinical negation detection? J Am Med Inform Assoc 27(4):584\u2013591","journal-title":"J Am Med Inform Assoc"},{"key":"5802_CR52","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-981-16-3153-5_53","volume":"2021","author":"V Dogra","year":"2021","unstructured":"Dogra V, Singh A et al (2021) Analyzing distilbert for sentiment classification of banking financial news. Intell Comput Innov Data Sci 2021:501\u2013510","journal-title":"Intell Comput Innov Data Sci"},{"key":"5802_CR53","doi-asserted-by":"crossref","unstructured":"Yao S, et\u00a0al. (2022) Reprbert: Distilling bert to an efficient representation-based relevance model for e-commerce. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","DOI":"10.1145\/3534678.3539090"},{"key":"5802_CR54","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1162\/tacl_a_00360","volume":"9","author":"X Wang","year":"2021","unstructured":"Wang X et al (2021) Kepler: a unified model for knowledge embedding and pre-trained language representation. Trans Assoc Comput Linguistic 9:176\u2013194","journal-title":"Trans Assoc Comput Linguistic"},{"key":"5802_CR55","doi-asserted-by":"crossref","unstructured":"Zhu R, Tu X, Huang JX (2021) Utilizing bert for biomedical and clinical text mining. In: Data analytics in biomedical engineering and healthcare. pp 73\u2013103","DOI":"10.1016\/B978-0-12-819314-3.00005-7"},{"issue":"8","key":"5802_CR56","doi-asserted-by":"publisher","first-page":"11003","DOI":"10.1007\/s13369-021-05810-5","volume":"48","author":"JA Alzubi","year":"2023","unstructured":"Alzubi JA et al (2023) Cobert: Covid-19 question answering system using bert. Arab J Sci Eng 48(8):11003\u201311013","journal-title":"Arab J Sci Eng"},{"key":"5802_CR57","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi L, Chiriatti M (2020) Gpt-3: its nature, scope, limits, and consequences. Mind Mach 30:681\u2013694","journal-title":"Mind Mach"},{"key":"5802_CR58","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: NAACL","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"5802_CR59","doi-asserted-by":"crossref","unstructured":"Marelli M, Menini S, Baroni M, et\u00a0al. (2014) SemEval-2014 task 1: evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. In: SemEval 2014","DOI":"10.3115\/v1\/S14-2001"},{"key":"5802_CR60","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, et\u00a0al. (2017) Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP. pp 452\u2013461","DOI":"10.18653\/v1\/D17-1047"},{"key":"5802_CR61","doi-asserted-by":"crossref","unstructured":"Li X, Bing L, Lam W, et\u00a0al. (2018) Transformation networks for target-oriented sentiment classification. In: ACL. pp 946\u2013956","DOI":"10.18653\/v1\/P18-1087"},{"key":"5802_CR62","doi-asserted-by":"crossref","unstructured":"Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: EMNLP. pp 3433\u20133442","DOI":"10.18653\/v1\/D18-1380"},{"key":"5802_CR63","doi-asserted-by":"crossref","unstructured":"Zhang M, Qian T (2020) Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: EMNLP. pp 3540\u20133549","DOI":"10.18653\/v1\/2020.emnlp-main.286"},{"key":"5802_CR64","doi-asserted-by":"crossref","unstructured":"Liang B, Yin R, Gui L, et\u00a0al. (2020) Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: COLING. pp 150\u2013161","DOI":"10.18653\/v1\/2020.coling-main.13"},{"key":"5802_CR65","unstructured":"Touvron H, et\u00a0al. (2023) Llama: open and efficient foundation language models. arXiv:2302.13971"},{"key":"5802_CR66","doi-asserted-by":"crossref","unstructured":"Wang Y, et\u00a0al. (2022) Self-instruct: aligning language models with self-generated instructions. arXiv:2212.10560","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"5802_CR67","doi-asserted-by":"crossref","unstructured":"Masalkhi M et al (2024) A side-by-side evaluation of llama 2 by meta with chatgpt and its application in ophthalmology. Eye 1\u20134","DOI":"10.1038\/s41433-024-02972-y"},{"key":"5802_CR68","unstructured":"Huang Y, et\u00a0al. (2024) C-eval: a multi-level multi-discipline chinese evaluation suite for foundation models. In: Advances in neural information processing systems"},{"key":"5802_CR69","doi-asserted-by":"crossref","unstructured":"Huang B, Ou Y, Carley KM (2018) Aspect level sentiment classification with attention-over-attention neural networks. In: SBP. Springer, pp 197\u2013206","DOI":"10.1007\/978-3-319-93372-6_22"},{"key":"5802_CR70","doi-asserted-by":"crossref","unstructured":"Xu L, Bing L, Lu W, et\u00a0al. (2020) Aspect sentiment classification with aspect-specific opinion spans. In: EMNLP. pp 3561\u20133567","DOI":"10.18653\/v1\/2020.emnlp-main.288"},{"key":"5802_CR71","doi-asserted-by":"crossref","unstructured":"Song Y, Wang J, Jiang T, et\u00a0al. (2019) Targeted sentiment classification with attentional encoder network. In: ICANN. Springer, pp 93\u2013103","DOI":"10.1007\/978-3-030-30490-4_9"},{"key":"5802_CR72","doi-asserted-by":"crossref","unstructured":"Han B, Chen Z, Qian Y (2022) Local information modeling with self-attention for speaker verification. In: ICASSP. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746050"},{"issue":"2","key":"5802_CR73","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TAFFC.2019.2945028","volume":"13","author":"T Yang","year":"2019","unstructured":"Yang T et al (2019) Aspect-based sentiment analysis with new target representation and dependency attention. IEEE Trans Affect Comput 13(2):640\u2013650","journal-title":"IEEE Trans Affect Comput"},{"issue":"2","key":"5802_CR74","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1049\/cit2.12086","volume":"8","author":"Z Tao","year":"2023","unstructured":"Tao Z et al (2023) Multi-head attention graph convolutional network model: End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network. CAAI Trans Intell Technol 8(2):468\u2013477","journal-title":"CAAI Trans Intell Technol"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05802-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05802-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05802-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T15:28:12Z","timestamp":1726673292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05802-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":74,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["5802"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05802-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"22 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}