{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T18:04:12Z","timestamp":1778522652141,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10489-024-06011-x","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T04:29:47Z","timestamp":1734496187000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["WRGAT-PTBERT: weighted relational graph attention network over post-trained BERT for aspect based sentiment analysis"],"prefix":"10.1007","volume":"55","author":[{"given":"Sharad","family":"Verma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2156-2104","authenticated-orcid":false,"given":"Ashish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditi","family":"Sharan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"issue":"8","key":"6011_CR1","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"143\u2013155","key":"6011_CR2","first-page":"18","volume":"19","author":"Y LeCun","year":"1989","unstructured":"LeCun Y et al (1989) Generalization and network design strategies. Connectionism Perspective 19(143\u2013155):18","journal-title":"Connectionism Perspective"},{"key":"6011_CR3","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"6011_CR4","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings"},{"key":"6011_CR5","doi-asserted-by":"crossref","unstructured":"Wang S, Mazumder S, Liu B, Zhou M, Chang Y (2018) Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers)","DOI":"10.18653\/v1\/P18-1088"},{"key":"6011_CR6","doi-asserted-by":"publisher","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 214\u2013224. Association for Computational Linguistics, Austin, Texas. https:\/\/doi.org\/10.18653\/v1\/D16-1021","DOI":"10.18653\/v1\/D16-1021"},{"key":"6011_CR7","doi-asserted-by":"publisher","unstructured":"Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. 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. 4568\u20134578. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1464","DOI":"10.18653\/v1\/D19-1464"},{"key":"6011_CR8","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":"6011_CR9","doi-asserted-by":"publisher","unstructured":"Huang B, Carley K (2019) Syntax-aware aspect level sentiment classification with graph attention networks. 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 5469\u20135477. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1549","DOI":"10.18653\/v1\/D19-1549"},{"key":"6011_CR10","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR)"},{"key":"6011_CR11","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph Attention Networks. International Conference on Learning Representations"},{"key":"6011_CR12","doi-asserted-by":"publisher","first-page":"119903","DOI":"10.1016\/j.ins.2023.119903","volume":"657","author":"Q Ke","year":"2024","unstructured":"Ke Q, Jing X, Wo\u017aniak M, Xu S, Liang Y, Zheng J (2024) Apgvae: Adaptive disentangled representation learning with the graph-based structure information. Inf Sci 657:119903","journal-title":"Inf Sci"},{"key":"6011_CR13","doi-asserted-by":"crossref","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: Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short Papers), pp 49\u201354","DOI":"10.3115\/v1\/P14-2009"},{"key":"6011_CR14","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) 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. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"6011_CR15","doi-asserted-by":"publisher","unstructured":"Xue W, Li T (2018) Aspect based sentiment analysis with gated convolutional networks. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 2514\u20132523. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-1234","DOI":"10.18653\/v1\/P18-1234"},{"key":"6011_CR16","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang R, Mensah S, Mao Y (2020) Replicate, walk, and stop on syntax: an effective neural network model for aspect-level sentiment classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 9685\u20139692","DOI":"10.1609\/aaai.v34i05.6517"},{"key":"6011_CR17","doi-asserted-by":"publisher","unstructured":"Xu H, Liu B, Shu L, Yu P (2019) BERT post-training for review reading comprehension and aspect-based sentiment analysis. 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 2324\u20132335. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1242","DOI":"10.18653\/v1\/N19-1242"},{"key":"6011_CR18","doi-asserted-by":"publisher","unstructured":"Xu H, Liu B, Shu L, Yu P (2020) DomBERT: Domain-oriented language model for aspect-based sentiment analysis. In: Cohn T, He Y, Liu Y (eds) Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1725\u20131731. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.156","DOI":"10.18653\/v1\/2020.findings-emnlp.156"},{"key":"6011_CR19","doi-asserted-by":"publisher","unstructured":"Li X, Bing L, Zhang W, Lam W (2019) Exploiting BERT for end-to-end aspect-based sentiment analysis. In: Xu W, Ritter A, Baldwin T, Rahimi A (eds) Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pp. 34\u201341. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-5505","DOI":"10.18653\/v1\/D19-5505"},{"key":"6011_CR20","doi-asserted-by":"crossref","unstructured":"Li X, Bing L, Li P, Lam W (2019) A unified model for opinion target extraction and target sentiment prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 6714\u20136721","DOI":"10.1609\/aaai.v33i01.33016714"},{"key":"6011_CR21","doi-asserted-by":"crossref","unstructured":"Song Y, Wang J, Jiang T, Liu Z, Rao Y (2019) Attentional encoder network for targeted sentiment classification. In: International conference on artificial neural networks","DOI":"10.1007\/978-3-030-30490-4_9"},{"key":"6011_CR22","doi-asserted-by":"publisher","first-page":"154290","DOI":"10.1109\/ACCESS.2019.2946594","volume":"7","author":"Z Gao","year":"2019","unstructured":"Gao Z, Feng A, Song X, Wu X (2019) Target-dependent sentiment classification with bert. IEEE Access 7:154290\u2013154299","journal-title":"IEEE Access"},{"key":"6011_CR23","doi-asserted-by":"publisher","unstructured":"Sun C, Huang L, Qiu X (2019) Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. 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 380\u2013385. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1035","DOI":"10.18653\/v1\/N19-1035"},{"key":"6011_CR24","doi-asserted-by":"publisher","unstructured":"Xu H, Shu L, Yu P, Liu B (2020) Understanding pre-trained BERT for aspect-based sentiment analysis. In: Scott D, Bel N, Zong C (eds) Proceedings of the 28th International Conference on Computational Linguistics, pp. 244\u2013250. International Committee on Computational Linguistics, Barcelona, Spain (Online). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.21","DOI":"10.18653\/v1\/2020.coling-main.21"},{"issue":"1","key":"6011_CR25","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"key":"6011_CR26","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":"6011_CR27","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":"6011_CR28","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1109\/TASLP.2020.3042009","volume":"29","author":"X Bai","year":"2020","unstructured":"Bai X, Liu P, Zhang Y (2020) Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network. IEEE\/ACM Trans Audio Speech Language Process 29:503\u2013514","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"6011_CR29","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":"6011_CR30","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15, Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"6011_CR31","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":"6011_CR32","doi-asserted-by":"publisher","unstructured":"Hou X, Huang J, Wang G, Qi P, He X, Zhou B (2021) Selective attention based graph convolutional networks for aspect-level sentiment classification. In: Panchenko A, Malliaros FD, Logacheva V, Jana A, Ustalov D, Jansen P (eds) Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pp 83\u201393. Association for Computational Linguistics, Mexico City, Mexico. https:\/\/doi.org\/10.18653\/v1\/2021.textgraphs-1.8","DOI":"10.18653\/v1\/2021.textgraphs-1.8"},{"key":"6011_CR33","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advan Neural Inform Process Syst 30"},{"key":"6011_CR34","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"key":"6011_CR35","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"6011_CR36","doi-asserted-by":"publisher","unstructured":"Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp 2227\u20132237. Association for Computational Linguistics, New Orleans, Louisiana. https:\/\/doi.org\/10.18653\/v1\/N18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"6011_CR37","doi-asserted-by":"publisher","unstructured":"Cho K, van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti A, Pang B, Daelemans W (eds) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1724\u20131734. Association for Computational Linguistics, Doha, Qatar. https:\/\/doi.org\/10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"6011_CR38","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":"6011_CR39","doi-asserted-by":"crossref","unstructured":"Jiang Q, Chen L, Xu R, Ao X, Yang M (2019) A challenge dataset and effective models for aspect-based sentiment analysis. 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 6280\u20136285","DOI":"10.18653\/v1\/D19-1654"},{"key":"6011_CR40","unstructured":"Dozat T, Manning CD (2017) Deep biaffine attention for neural dependency parsing. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=Hk95PK9le"},{"key":"6011_CR41","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. IJCAI\u201917, AAAI Press, Melbourne, Australia, pp 4068\u20134074","DOI":"10.24963\/ijcai.2017\/568"},{"key":"6011_CR42","doi-asserted-by":"crossref","unstructured":"Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433\u20133442","DOI":"10.18653\/v1\/D18-1380"},{"key":"6011_CR43","doi-asserted-by":"crossref","unstructured":"Nguyen HT, Le Nguyen M (2018) Effective attention networks for aspect-level sentiment classification. In: 2018 10th International Conference on Knowledge and Systems Engineering (KSE), IEEE, pp 25\u201330","DOI":"10.1109\/KSE.2018.8573324"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06011-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06011-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06011-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T16:02:05Z","timestamp":1738252925000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06011-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["6011"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06011-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"30 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 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":"NA.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests\/Competing interests"}},{"value":"NA.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"NA","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial interests"}}],"article-number":"181"}}