{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:27:39Z","timestamp":1782520059963,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, \u201cThe food is tasty but restaurant is untidy\u201d, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an <jats:inline-formula><jats:alternatives><jats:tex-math>$$aspect$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>aspect<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$context$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>context<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.<\/jats:p>","DOI":"10.1007\/s11063-024-11513-3","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T17:02:07Z","timestamp":1708102927000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification"],"prefix":"10.1007","volume":"56","author":[{"given":"P. R. Joe","family":"Dhanith","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Surendiran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Rohith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujithra R.","family":"Kanmani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Valli","family":"Devi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"11513_CR1","doi-asserted-by":"crossref","unstructured":"Ambartsoumian A, Popowich F (2019) Self-attention: a better building block for sentiment analysis neural network classifiers. 130\u2013139","DOI":"10.18653\/v1\/W18-6219"},{"key":"11513_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110559","volume":"270","author":"P Wang","year":"2023","unstructured":"Wang P et al (2023) A novel adaptive marker segmentation graph convolutional network for aspect-level sentiment analysis. Knowledge-Based Syst 270:110559","journal-title":"Knowledge-Based Syst"},{"key":"11513_CR3","first-page":"1","volume":"3045","author":"A Nazir","year":"2020","unstructured":"Nazir A, Rao Y, Wu L, Sun L (2020) Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans Affect Comput 3045:1\u201320","journal-title":"IEEE Trans Affect Comput"},{"key":"11513_CR4","doi-asserted-by":"crossref","unstructured":"Dhanith PRJ, Prabha KSS (2023) A critical empirical evaluation of deep learning models for solving aspect based sentiment analysis. Artif Intell Rev","DOI":"10.1007\/s10462-023-10460-0"},{"key":"11513_CR5","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhao L, Zhu X (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"},{"issue":"9","key":"11513_CR6","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423\u20131447","journal-title":"Proc IEEE"},{"key":"11513_CR7","doi-asserted-by":"publisher","first-page":"78454","DOI":"10.1109\/ACCESS.2019.2920075","volume":"7","author":"J Zhou","year":"2019","unstructured":"Zhou J, Huang JX, Chen Q, Hu QV, Wang T, He L (2019) Deep learning for aspect-level sentiment classification: Survey, vision, and challenges. IEEE Access 7:78454\u201378483","journal-title":"IEEE Access"},{"issue":"6","key":"11513_CR8","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/TCSS.2020.3033302","volume":"7","author":"H Liu","year":"2020","unstructured":"Liu H, Chatterjee I, Zhou M, Lu XS, Abusorrah A (2020) Aspect-based sentiment analysis: A survey of deep learning methods. IEEE Trans Comput Soc Syst 7(6):1358\u20131375","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"11513_CR9","unstructured":"Goldberg Y, Levy O (2014) word2vec explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722.."},{"key":"11513_CR10","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26"},{"key":"11513_CR11","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st international conference on learning representation ICLR 2013 - Work. Track Proc., pp 1\u201312"},{"key":"11513_CR12","doi-asserted-by":"crossref","unstructured":"Pennington J, Richard S (2017) GloVe: global vectors for word representation Jeffrey. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"issue":"January","key":"11513_CR13","first-page":"3104","volume":"4","author":"I Sutskever","year":"2014","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 4(January):3104\u20133112","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"11513_CR14","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1007\/s13042-018-0799-4","volume":"10","author":"M Al-Smadi","year":"2019","unstructured":"Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y (2019) Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybern 10(8):2163\u20132175","journal-title":"Int J Mach Learn Cybern"},{"key":"11513_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107136","volume":"226","author":"RK Yadav","year":"2021","unstructured":"Yadav RK, Jiao L, Goodwin M, Granmo OC (2021) Positionless aspect based sentiment analysis using attention mechanism[Formula presented]. Knowledge-Based Syst 226:107136","journal-title":"Knowledge-Based Syst"},{"key":"11513_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107736","volume":"236","author":"H Wu","year":"2022","unstructured":"Wu H, Zhang Z, Shi S, Wu Q, Song H (2022) Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis. Knowledge-Based Syst 236:107736","journal-title":"Knowledge-Based Syst"},{"key":"11513_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103477","volume":"296","author":"J Su","year":"2021","unstructured":"Su J et al (2021) Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning. Artif Intell 296:103477","journal-title":"Artif Intell"},{"key":"11513_CR18","unstructured":"Shu L, Xu H, Liu B (2019) Controlled CNN-based sequence labeling for aspect extraction."},{"issue":"3","key":"11513_CR19","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1093\/comjnl\/bxz031","volume":"63","author":"G Liu","year":"2020","unstructured":"Liu G, Huang X, Liu X, Yang A (2020) A novel aspect-based sentiment analysis network model based on multilingual hierarchy in online social network. Comput J 63(3):410\u2013424","journal-title":"Comput J"},{"key":"11513_CR20","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neucom.2021.03.092","volume":"450","author":"X Wang","year":"2021","unstructured":"Wang X, Li F, Zhang Z, Xu G, Zhang J, Sun X (2021) A unified position-aware convolutional neural network for aspect based sentiment analysis. Neurocomputing 450:91\u2013103","journal-title":"Neurocomputing"},{"key":"11513_CR21","unstructured":"Karimi A, Rossi L, Prati A (2020) Improving BERT performance for aspect-based sentiment analysis."},{"key":"11513_CR22","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics vol 1, pp 4171\u20134186"},{"key":"11513_CR23","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, Meng F, Zhang J, Chen Y, Xu J, Zhou J (2021) A dependency syntactic knowledge augmented interactive architecture for end-to-end aspect-based sentiment analysis. Neurocomputing 454:291\u2013302","journal-title":"Neurocomputing"},{"key":"11513_CR24","doi-asserted-by":"crossref","unstructured":"Kiritchenko S, Zhu X, Cherry C, Mohammad S (2015) NRC-Canada-2014: detecting aspects and sentiment in customer reviews. No. SemEval, pp 437\u2013442","DOI":"10.3115\/v1\/S14-2076"},{"key":"11513_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Shirai K (2015) PhraseRNN: Phrase recursive neural network for aspect-based sentiment analysis. In: Conf. Proc. - EMNLP 2015 Conf. Empir. Methods Nat. Lang. Process., no. September, pp. 2509\u20132514, 2015.","DOI":"10.18653\/v1\/D15-1298"},{"issue":"7","key":"11513_CR26","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1109\/TASLP.2019.2913094","volume":"27","author":"H Luo","year":"2019","unstructured":"Luo H, Li T, Liu B, Wang B, Unger H (2019) Improving aspect term extraction with bidirectional dependency tree representation. IEEE\/ACM Trans Audio Speech Lang Process 27(7):1201\u20131212","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"4","key":"11513_CR27","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/JAS.2020.1003243","volume":"7","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Xu B, Zhao T (2020) Convolutional multi-head self-attention on memory for aspect sentiment classification. IEEE\/CAA J Autom Sin 7(4):1038\u20131044","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"11513_CR28","doi-asserted-by":"crossref","unstructured":"Kumar A, Teja Narapareddy V, Aditya Srikanth V, Bhanu Murthy Neti L, Malapati A (2020) Special section on advanced data mining methods for social computing aspect-based sentiment classification using interactive gated convolutional network. pp 22445\u201322453","DOI":"10.1109\/ACCESS.2020.2970030"},{"key":"11513_CR29","first-page":"5876","volume":"2018","author":"Y Ma","year":"2018","unstructured":"Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. 32nd AAAI Conf Artif Intell AAAI 2018:5876\u20135883","journal-title":"32nd AAAI Conf Artif Intell AAAI"},{"key":"11513_CR30","doi-asserted-by":"crossref","unstructured":"Giannakopoulos A, Musat C, Hossmann A, Baeriswyl M (2017) Unsupervised aspect term extraction with B-LSTM & CRF using automatically labelled datasets. arXiv preprint arXiv:1709.05094..","DOI":"10.18653\/v1\/W17-5224"},{"key":"11513_CR31","doi-asserted-by":"publisher","first-page":"20462","DOI":"10.1109\/ACCESS.2019.2893806","volume":"7","author":"J Zeng","year":"2019","unstructured":"Zeng J, Ma X, Zhou K (2019) enhancing attention-based LSTM with position context for aspect-level sentiment classification. IEEE Access 7:20462\u201320471","journal-title":"IEEE Access"},{"issue":"5","key":"11513_CR32","first-page":"392","volume":"13","author":"EI Setiawan","year":"2020","unstructured":"Setiawan EI, Ferry F, Santoso J, Sumpeno S, Fujisawa K, Purnomo MH (2020) Bidirectional GRU for targeted aspect-based sentiment analysis based on character-enhanced token-embedding and multi-level attention. Int J Intell Eng Syst 13(5):392\u2013407","journal-title":"Int J Intell Eng Syst"},{"key":"11513_CR33","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":"11513_CR34","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of 45th annual meeting of the association for computational linguistics (ACL), vol 1, 1556\u20131566","DOI":"10.3115\/v1\/P15-1150"},{"key":"11513_CR35","unstructured":"Liang Y, Meng F, Zhang J, Xu J, Chen Y, Zhou J (2020) A novel aspect-guided deep transition model for aspect based sentiment analysis. In: Proceedings of the 2020 conference on empirical methods in natural language processing, pp 5569\u20135580"},{"key":"11513_CR36","unstructured":"Tang D, Qin B, Feng X, Liu T (2015) Effective LSTMs for target-dependent sentiment classification. In: COLING 2016 - 26th international conference on computing linguistics, pp 3298\u20133307"},{"issue":"4","key":"11513_CR37","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/s12559-018-9549-x","volume":"10","author":"Y Ma","year":"2018","unstructured":"Ma Y, Peng H, Khan T, Cambria E, Hussain A (2018) Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cognit Comput 10(4):639\u2013650","journal-title":"Cognit Comput"},{"key":"11513_CR38","doi-asserted-by":"crossref","unstructured":"Li L, Liu Y, Zhou A (2018) Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of the 22nd conference on computational natural language learning, pp 181\u2013189","DOI":"10.18653\/v1\/K18-1018"},{"key":"11513_CR39","unstructured":"Zheng S, Xia R (2018) Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention"},{"key":"11513_CR40","first-page":"442","volume-title":"International conference on computational linguistics and intelligent text processing 2018 Mar 18","author":"A Laddha","year":"2019","unstructured":"Laddha A, Mukherjee A (2019) Aspect specific opinion expression extraction using attention based LSTM-CRF network. International conference on computational linguistics and intelligent text processing 2018 Mar 18. Springer, Cham, pp 442\u2013454"},{"key":"11513_CR41","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1109\/TASLP.2021.3058540","volume":"29","author":"P Lin","year":"2021","unstructured":"Lin P, Yang M, Lai J (2021) Deep selective memory network with selective attention and inter-aspect modeling for aspect level sentiment classification. IEEE\/ACM Trans Audio Speech Lang Process 29:1093\u20131106","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"11513_CR42","doi-asserted-by":"crossref","unstructured":"Zhang C, Li Q, Song D (2020) Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: EMNLP-IJCNLP 2019\u20132019 conference\u00a0on\u00a0empirical methods\u00a0in natural language processing, pp 4568\u20134578","DOI":"10.18653\/v1\/D19-1464"},{"key":"11513_CR43","doi-asserted-by":"crossref","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. 83\u201393","DOI":"10.18653\/v1\/2021.textgraphs-1.8"},{"key":"11513_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107073","volume":"223","author":"C Wu","year":"2021","unstructured":"Wu C et al (2021) Multiple-element joint detection for aspect-based sentiment analysis. Knowledge-Based Syst 223:107073","journal-title":"Knowledge-Based Syst"},{"key":"11513_CR45","first-page":"4543","volume":"2020","author":"AP Ben Veyseh","year":"2020","unstructured":"Ben Veyseh AP, 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. Find Assoc Comput Linguist EMNLP 2020:4543\u20134548","journal-title":"Find Assoc Comput Linguist EMNLP"},{"key":"11513_CR46","doi-asserted-by":"crossref","unstructured":"Wang K, Shen W, Yang Y, Quan X, Wang R (2020) Relational graph attention network for aspect-based sentiment analysis. 3229\u20133238","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"11513_CR47","unstructured":"Duchi J, Hazan E, Singer Y (2010) Adaptive subgradient methods for online learning and stochastic optimization. In: COLT 2010 - 23rd conference on learning theory, pp 257\u2013269"},{"key":"11513_CR48","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: 52nd annual meeting association computing, linguistics ACL 2014, vol 2, pp 49\u201354","DOI":"10.3115\/v1\/P14-2009"},{"key":"11513_CR49","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2015) SemEval-2014 Task 4: aspect based sentiment analysis. no. SemEval, pp 27\u201335","DOI":"10.3115\/v1\/S14-2004"},{"key":"11513_CR50","doi-asserted-by":"crossref","unstructured":"Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2015) SemEval-2015 task 12: aspect based sentiment analysis, 486\u2013495","DOI":"10.18653\/v1\/S15-2082"},{"key":"11513_CR51","doi-asserted-by":"crossref","unstructured":"Pontiki M et al (2016) SemEval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on semantic evaluation, pp 19\u201330","DOI":"10.18653\/v1\/S16-1002"},{"key":"11513_CR52","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 twenty-sixth international joint conference artificial intelligence, pp 4068\u20134074","DOI":"10.24963\/ijcai.2017\/568"},{"key":"11513_CR53","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: EMNLP 2016 \u2013 conference on empirical methods natural language processing, 214\u2013224","DOI":"10.18653\/v1\/D16-1021"},{"key":"11513_CR54","doi-asserted-by":"crossref","unstructured":"Huang B, Ou Y, Carley KM (2018) Aspect level sentiment classification with attention-over-attention neural networks. In: Social, cultural, and behavioral modeling: 11th international conference, SBP-BRiMS 2018, Washington, DC, USA, July 10\u201313, 2018, Proceedings 11","DOI":"10.1007\/978-3-319-93372-6_22"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11513-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11513-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11513-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T20:20:26Z","timestamp":1715890826000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11513-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["11513"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11513-3","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"24 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"47"}}