{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:09:31Z","timestamp":1783789771486,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.71502125"],"award-info":[{"award-number":["No.71502125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s12559-022-10004-8","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T09:06:40Z","timestamp":1644224800000},"page":"1039-1054","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Social Media Sentiment Analysis Based on Dependency Graph and Co-occurrence Graph"],"prefix":"10.1007","volume":"14","author":[{"given":"Zhigang","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manyue","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"10004_CR1","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1007\/978-1-4899-7687-1_907","volume-title":"Encyclopedia of Machine Learning and Data Mining","author":"L Zhang","year":"2017","unstructured":"Zhang L, Liu B. Sentiment analysis and opinion mining. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning and Data Mining. Boston: Springer; 2017. p. 1152\u201361. https:\/\/doi.org\/10.1007\/978-1-4899-7687-1_907."},{"key":"10004_CR2","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint; 2013. https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"10004_CR3","doi-asserted-by":"publisher","unstructured":"Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing. Association for Computational Linguistics; 2014. p. 1532\u201343. https:\/\/doi.org\/10.3115\/v1\/D14-1162.","DOI":"10.3115\/v1\/D14-1162"},{"key":"10004_CR4","doi-asserted-by":"crossref","unstructured":"Felbo B, Mislove A, S\u00f8gaard A, Rahwan I, Lehmann S. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: 2017 Conference on Empirical Methods in Natural Language Processing. EMNLP; 2017. p. 1615\u201325.","DOI":"10.18653\/v1\/D17-1169"},{"key":"10004_CR5","doi-asserted-by":"publisher","unstructured":"Chen Y, Yuan J, You Q, Luo J. Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In: Proceedings of the 26th ACM international conference on Multimedia. New York: Association for Computing Machinery; 2018. p. 117\u201325. https:\/\/doi.org\/10.1145\/3240508.3240533.","DOI":"10.1145\/3240508.3240533"},{"key":"10004_CR6","doi-asserted-by":"publisher","unstructured":"Tao Y, Zhang X, Shi L, Wei L, Hai Z, Wahid J A. Joint embedding of emoticons and labels based on CNN for microblog sentiment analysis. In: Proceedings of the Fourth IEEE International Conference on Data Science in Cyberspace. 2019. p. 168\u201375. https:\/\/doi.org\/10.1109\/DSC.2019.00033.","DOI":"10.1109\/DSC.2019.00033"},{"key":"10004_CR7","doi-asserted-by":"publisher","first-page":"104348","DOI":"10.1016\/j.cognition.2020.104348","volume":"203","author":"E Fedorenko","year":"2020","unstructured":"Fedorenko E, Blank IA, Siegelman M, Mineroff Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition. 2020;203:104348.","journal-title":"Cognition."},{"key":"10004_CR8","doi-asserted-by":"publisher","first-page":"103427","DOI":"10.1016\/j.artint.2020.103427","volume":"292","author":"M Zhang","year":"2021","unstructured":"Zhang M, Li Z, Fu G, Zhang M. Dependency-based syntax-aware word representations. Artif Intell. 2021;292:103427.","journal-title":"Artif Intell."},{"key":"10004_CR9","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2021.06.040","volume":"457","author":"J Zeng","year":"2021","unstructured":"Zeng J, Liu T, Jia W, Zhou J. Fine-grained question-answer sentiment classification with hierarchical graph attention network. Neurocomputing. 2021;457:214\u201324.","journal-title":"Neurocomputing"},{"issue":"2","key":"10004_CR10","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MIS.2016.31","volume":"30","author":"E Cambria","year":"2016","unstructured":"Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;30(2):102\u20137.","journal-title":"IEEE Intell Syst"},{"key":"10004_CR11","unstructured":"Nielsen F\u00c5. A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC2011 Workshop on \u2018Making Sense of Microposts\u2019: big things come in small packages, vol. 718. 2011. p. 93\u20138."},{"key":"10004_CR12","doi-asserted-by":"publisher","unstructured":"Oraby S, El-Sonbaty Y, El-Nasr MA. Finding opinion strength using rule-based parsing for Arabic sentiment analysis. In: Castro F, Gelbukh A, Gonz\u00e1lez M, editors. MICAI 2013: Advances in Soft Computing and Its Applications, vol. 8266. 2013. p. 509\u201320. https:\/\/doi.org\/10.1007\/978-3-642-45111-9_44.","DOI":"10.1007\/978-3-642-45111-9_44"},{"key":"10004_CR13","doi-asserted-by":"publisher","unstructured":"Cambria E. An introduction to concept-level sentiment analysis. In: Castro F, Gelbukh A, Gonz\u00e1lez M, editors. MICAI 2013: Advances in Soft Computing and Its Applications, vol. 8266. Berlin: Springer; 2013. p. 478\u201383. https:\/\/doi.org\/10.1007\/978-3-642-45111-9_41.","DOI":"10.1007\/978-3-642-45111-9_41"},{"issue":"9","key":"10004_CR14","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1108\/IMDS-12-2017-0582","volume":"118","author":"M Li","year":"2018","unstructured":"Li M, Ch\u2019ng E, Chong A, See S. Multi-class Twitter sentiment classification with emojis. Ind Manag Data Syst. 2018;118(9):1804\u201320. https:\/\/doi.org\/10.1108\/IMDS-12-2017-0582.","journal-title":"Ind Manag Data Syst"},{"key":"10004_CR15","doi-asserted-by":"publisher","unstructured":"Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2014. p. 1746\u201351. https:\/\/doi.org\/10.3115\/v1\/d14-1181.","DOI":"10.3115\/v1\/d14-1181"},{"key":"10004_CR16","doi-asserted-by":"publisher","first-page":"13949","DOI":"10.1109\/ACCESS.2018.2814818","volume":"6","author":"A Hassan","year":"2018","unstructured":"Hassan A, Mahmood A. Convolutional recurrent deep learning model for sentence classification. IEEE Access. 2018;6:13949\u201357. https:\/\/doi.org\/10.1109\/ACCESS.2018.2814818.","journal-title":"IEEE Access"},{"issue":"8","key":"10004_CR17","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. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735.","journal-title":"Neural Comput"},{"key":"10004_CR18","doi-asserted-by":"publisher","first-page":"51522","DOI":"10.1109\/ACCESS.2019.2909919","volume":"7","author":"G Xu","year":"2019","unstructured":"Xu G, Meng Y, Qiu X, Yu Z, Wu X. Sentiment analysis of comment texts based on BiLSTM. IEEE Access. 2019;7:51522\u201332. https:\/\/doi.org\/10.1109\/ACCESS.2019.2909919.","journal-title":"IEEE Access"},{"key":"10004_CR19","doi-asserted-by":"publisher","unstructured":"Cho K, Merrienboer BV, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2014. p. 1724\u201334. https:\/\/doi.org\/10.3115\/v1\/D14-1179.","DOI":"10.3115\/v1\/D14-1179"},{"key":"10004_CR20","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1007\/s40747-021-00436-4","volume":"7","author":"J Shobana","year":"2021","unstructured":"Shobana J, Murali M. An efficient sentiment analysis methodology based on long short-term memory networks. Complex Intell Syst. 2021;7:2485\u2013501. https:\/\/doi.org\/10.1007\/s40747-021-00436-4.","journal-title":"Complex Intell Syst"},{"key":"10004_CR21","doi-asserted-by":"publisher","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B. Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2016. vol. 2, p. 207\u2013212. https:\/\/doi.org\/10.18653\/v1\/P16-2034.","DOI":"10.18653\/v1\/P16-2034"},{"key":"10004_CR22","doi-asserted-by":"publisher","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies: Human Language Technologies. Association for Computational Linguistics; 2016. p. 1480\u20139. https:\/\/doi.org\/10.18653\/v1\/N16-1174.","DOI":"10.18653\/v1\/N16-1174"},{"key":"10004_CR23","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri ME, Nemati S, Abdar M, Cambria C, Acharya UR. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Gener Comput Syst. 2021;115:279\u201394.","journal-title":"Future Gener Comput Syst"},{"key":"10004_CR24","doi-asserted-by":"publisher","first-page":"107258","DOI":"10.1016\/j.knosys.2021.107258","volume":"228","author":"W Li","year":"2021","unstructured":"Li W, Zhu L, Cambria E. Taylor\u2019s theorem: a new perspective for neural tensor networks. Knowl Based Syst. 2021;228:107258.","journal-title":"Knowl Based Syst"},{"key":"10004_CR25","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates, Inc.; 2017. p. 5998\u20136008."},{"key":"10004_CR26","doi-asserted-by":"publisher","unstructured":"Jiang F, Cui A, Liu Y, Ma S. Every term has sentiment: learning from emoticon evidences for Chinese microblog sentiment analysis. In: Zhou G, Li J, Zhao D, Feng Y, editors. Proceedings of the second CCF conference on Natural Language Processing and Chinese Computing, vol. 400. Heidelberg: Springer; 2013. p. 224\u2013235. https:\/\/doi.org\/10.1007\/978-3-642-41644-6_21.","DOI":"10.1007\/978-3-642-41644-6_21"},{"key":"10004_CR27","doi-asserted-by":"crossref","unstructured":"Cambria E, Li Y, Xing FZ , Poria S, Kwok K. SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: The 29th ACM International Conference on Information and Knowledge Management. New York: Association for Computing Machinery; 2020. p. 105\u201314.","DOI":"10.1145\/3340531.3412003"},{"key":"10004_CR28","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1016\/j.asoc.2017.07.056","volume":"68","author":"M Rathan","year":"2017","unstructured":"Rathan M, Hulipalled VR, Venugopal KR, Patnaik LM. Consumer insight mining: aspect based Twitter opinion mining of mobile phone reviews. Appl Soft Comput. 2017;68:765\u201373. https:\/\/doi.org\/10.1016\/j.asoc.2017.07.056.","journal-title":"Appl Soft Comput"},{"issue":"6","key":"10004_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102290","volume":"57","author":"D Li","year":"2020","unstructured":"Li D, Rzepka R, Ptaszynski M, Araki K. HEMOS: a novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Inf Process Manag. 2020;57(6): 102290. https:\/\/doi.org\/10.1016\/j.ipm.2020.102290.","journal-title":"Inf Process Manag"},{"key":"10004_CR30","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of Advances in Neural Information Processing Systems. MIT Press; 2001. p. 585\u201391.","DOI":"10.7551\/mitpress\/1120.003.0080"},{"issue":"5500","key":"10004_CR31","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"J Tenenbaum","year":"2000","unstructured":"Tenenbaum J, Silva VD, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science. 2000;290(5500):2319\u201323.","journal-title":"Science"},{"issue":"5500","key":"10004_CR32","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"S Roweis","year":"2000","unstructured":"Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science. 2000;290(5500):2323\u20136. https:\/\/doi.org\/10.1126\/science.290.5500.2323.","journal-title":"Science"},{"key":"10004_CR33","doi-asserted-by":"publisher","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W. Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery; 2016. p. 1105\u201314. https:\/\/doi.org\/10.1145\/2939672.2939751.","DOI":"10.1145\/2939672.2939751"},{"key":"10004_CR34","doi-asserted-by":"publisher","unstructured":"Perozzi B, AI-Rfou R, Skiena S. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery; 2014. p. 701\u201310. https:\/\/doi.org\/10.1145\/2623330.2623732.","DOI":"10.1145\/2623330.2623732"},{"key":"10004_CR35","doi-asserted-by":"publisher","unstructured":"Grover A, Leskovec J. Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery; 2016. p. 855\u201364. https:\/\/doi.org\/10.1145\/2939672.2939754.","DOI":"10.1145\/2939672.2939754"},{"key":"10004_CR36","doi-asserted-by":"publisher","unstructured":"Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery; 2016. p. 1225\u201334. https:\/\/doi.org\/10.1145\/2939672.2939753.","DOI":"10.1145\/2939672.2939753"},{"key":"10004_CR37","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint; 2017. https:\/\/arxiv.org\/abs\/1609.02907."},{"key":"10004_CR38","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"ESWC 2018: The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Berg RV, Welling M. Modeling relational data with graph convolutional networks. In: Gangemi A, et al., editors. ESWC 2018: The Semantic Web, vol. 10843. Cham: Springer; 2018. p. 593\u2013607. https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38."},{"key":"10004_CR39","unstructured":"Berg R, Kipf TN, Welling M. Graph convolutional matrix completion. arXiv preprint; 2017. https:\/\/arxiv.org\/abs\/1706.02263."},{"key":"10004_CR40","unstructured":"Bruna J, Zaremba W, Szlam A, Lecun Y. Spectral networks and locally connected networks on graphs. In: Proceedings of the 2nd International Conference on Learning Representations; 2014."},{"key":"10004_CR41","doi-asserted-by":"crossref","unstructured":"Li R, Wang S, Zhu F, Huang J. Adaptive graph convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32(1). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/11691.","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"10004_CR42","doi-asserted-by":"publisher","unstructured":"Zhuang C, Ma Q. Dual graph convolutional networks for graph-based semi-supervised classification. In: Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee; 2018. p. 499\u2013508. https:\/\/doi.org\/10.1145\/3178876.3186116.","DOI":"10.1145\/3178876.3186116"},{"key":"10004_CR43","unstructured":"Hamilton W, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS; 2017. p. 1025\u201335."},{"key":"10004_CR44","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations; 2018."},{"key":"10004_CR45","doi-asserted-by":"publisher","unstructured":"Yao L, Mao C, Luo Y. Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33(1):7370\u20137. https:\/\/doi.org\/10.1609\/aaai.v33i01.33017370.","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"10004_CR46","doi-asserted-by":"publisher","unstructured":"Huang L, Ma D, Li S, Zhang X, Wang H. Text level graph neural network for text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics; 2019. p. 3442\u20138. https:\/\/doi.org\/10.18653\/v1\/D19-1345.","DOI":"10.18653\/v1\/D19-1345"},{"key":"10004_CR47","unstructured":"Gu S, Zhang L, Hou Y, Song Y. A position-aware bidirectional attention net-work for aspect-level sentiment analysis. In: Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics; 2018. p. 774\u201384."},{"key":"10004_CR48","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"ESWC 2018: The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Berg RV, Titov I, Welling M. Modeling relational data with graph convolutional networks. In: Gangemi A, et al., editors. ESWC 2018: The Semantic Web. Cham: Springer; 2018. p. 593\u2013607. https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38."},{"key":"10004_CR49","doi-asserted-by":"publisher","unstructured":"Zhang M, Qian T. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2020. p. 3540\u20139. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.286.","DOI":"10.18653\/v1\/2020.emnlp-main.286"},{"key":"10004_CR50","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H, Shi C, Wang B, Cui P, Yu PS, et al. Heterogeneous graph attention network. In: The World Wide Web Conference (WWW '19). Association for Computing Machinery; 2019. p. 2022\u201332. https:\/\/doi.org\/10.1145\/3308558.3313562.","DOI":"10.1145\/3308558.3313562"},{"key":"10004_CR51","doi-asserted-by":"crossref","unstructured":"Bhagat R, Muralidharan S, Lobzhanidze A, Vishwanath S. Buy it again: Modeling repeat purchase recommendations. In: KDD. Association for Computing Machinery; 2018. p. 62\u201370.","DOI":"10.1145\/3219819.3219891"},{"key":"10004_CR52","doi-asserted-by":"publisher","unstructured":"Zhang Y, Yu X, Cui Z, Wu S, Wen Z, Wang L. Every document owns its structure: inductive text classification via graph neural networks. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACL; 2020. p. 334\u20139. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.31.","DOI":"10.18653\/v1\/2020.acl-main.31"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10004-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-022-10004-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10004-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T03:07:46Z","timestamp":1700190466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-022-10004-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["10004"],"URL":"https:\/\/doi.org\/10.1007\/s12559-022-10004-8","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,7]]},"assertion":[{"value":"11 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}