{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:44:03Z","timestamp":1776415443372,"version":"3.51.2"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"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":["J Supercomput"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11227-022-04950-1","type":"journal-article","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T17:03:57Z","timestamp":1668877437000},"page":"6991-7013","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Two-channel hierarchical attention mechanism model for short text classification"],"prefix":"10.1007","volume":"79","author":[{"given":"Guanghui","family":"Chang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-597X","authenticated-orcid":false,"given":"Shiyang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"4950_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Zhai C (2012) A survey of text classification algorithms. In: Mining text data, Springer, Boston, pp 163\u2013222","DOI":"10.1007\/978-1-4614-3223-4_6"},{"key":"4950_CR2","doi-asserted-by":"crossref","unstructured":"Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759","DOI":"10.18653\/v1\/E17-2068"},{"issue":"4","key":"4950_CR3","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/info10040150","volume":"10","author":"K Kowsari","year":"2019","unstructured":"Kowsari K, Jafari Meimandi K, Heidarysafa M, Mendu S, Barnes L, Brown D (2019) Text classification algorithms: a survey. Information 10(4):150","journal-title":"Information"},{"issue":"1","key":"4950_CR4","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1017\/S1351324916000334","volume":"23","author":"KW Church","year":"2017","unstructured":"Church KW (2017) Word2Vec. Nat Lang Eng 23(1):155\u2013162","journal-title":"Nat Lang Eng"},{"key":"4950_CR5","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":"4950_CR6","unstructured":"Joulin A, Grave E, Bojanowski P, Douze M, J\u00e9gou H, Mikolov T (2016) Fasttext. zip: compressing text classification models. arXiv preprint arXiv:1612.03651"},{"issue":"3","key":"4950_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2021","unstructured":"Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2021) Deep learning-based text classification: a comprehensive review. ACM Comput Surv (CSUR) 54(3):1\u201340","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2","key":"4950_CR8","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s13042-020-01175-7","volume":"12","author":"J Liu","year":"2021","unstructured":"Liu J, Zheng S, Xu G, Lin M (2021) Cross-domain sentiment aware word embeddings for review sentiment analysis. Int J Mach Learn Cybern 12(2):343\u2013354","journal-title":"Int J Mach Learn Cybern"},{"key":"4950_CR9","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"4950_CR10","unstructured":"Vaswani A, Shazeer N, Parmar Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"issue":"10","key":"4950_CR11","first-page":"2902","volume":"33","author":"C Huiping","year":"2016","unstructured":"Huiping C, Lidan W, Shukai D (2016) Sentiment classification model based on word embedding and CNN. Appl Res Comput 33(10):2902\u20132905","journal-title":"Appl Res Comput"},{"issue":"10","key":"4950_CR12","first-page":"2794","volume":"36","author":"C Liu","year":"2016","unstructured":"Liu C, Li X, Liu R, Fan X, Du L (2016) Chinese word segment based on character representation learning. J Comput Appl 36(10):2794","journal-title":"J Comput Appl"},{"key":"4950_CR13","doi-asserted-by":"publisher","first-page":"113898","DOI":"10.1016\/j.eswa.2020.113898","volume":"165","author":"DS Moirangthem","year":"2021","unstructured":"Moirangthem DS, Lee M (2021) Hierarchical and lateral multiple timescales gated recurrent units with pre-trained encoder for long text classification. Expert Syst Appl 165:113898","journal-title":"Expert Syst Appl"},{"key":"4950_CR14","doi-asserted-by":"publisher","first-page":"101182","DOI":"10.1016\/j.csl.2020.101182","volume":"68","author":"J Deng","year":"2021","unstructured":"Deng J, Cheng L, Wang Z (2021) Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification. Comput Speech Lang 68:101182","journal-title":"Comput Speech Lang"},{"issue":"1","key":"4950_CR15","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3233\/JIFS-191171","volume":"39","author":"S Yu","year":"2020","unstructured":"Yu S, Liu D, Zhu W, Zhang Y, Zhao S (2020) Attention-based LSTM, GRU and CNN for short text classification. J Intell Fuzzy Syst 39(1):333\u2013340","journal-title":"J Intell Fuzzy Syst"},{"key":"4950_CR16","unstructured":"Liu Jun, Li Wei, Chen Shuyu, Xu Guangxia (2022) PCA feature extraction algorithm based on anisotropic Gaussian kernel penalty, J Softw pp 1\u201316"},{"key":"4950_CR17","unstructured":"Pappas N, Popescu-Belis A (2017) Multilingual hierarchical attention networks for document classification. arXiv preprint arXiv:1707.00896"},{"key":"4950_CR18","unstructured":"Amin MZ, Nadeem N (2018) Convolutional neural network: text classification model for open domain question answering system. arXiv preprint arXiv:1809.02479"},{"key":"4950_CR19","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338","journal-title":"Neurocomputing"},{"issue":"1","key":"4950_CR20","doi-asserted-by":"publisher","first-page":"367","DOI":"10.2991\/ijcis.d.201207.001","volume":"14","author":"H Wang","year":"2021","unstructured":"Wang H, Tian K, Wu Z, Wang L (2021) A short text classification method based on convolutional neural network and semantic extension. Int J Comput Intell Syst 14(1):367\u2013375","journal-title":"Int J Comput Intell Syst"},{"key":"4950_CR21","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neucom.2019.08.080","volume":"386","author":"J Xu","year":"2020","unstructured":"Xu J, Cai Y, Wu X, Lei X, Huang Q, Leung HF, Li Q (2020) Incorporating context-relevant concepts into convolutional neural networks for short text classification. Neurocomputing 386:42\u201353","journal-title":"Neurocomputing"},{"key":"4950_CR22","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.ins.2020.10.021","volume":"548","author":"Y Liang","year":"2021","unstructured":"Liang Y, Li H, Guo B, Yu Z, Zheng X, Samtani S, Zeng DD (2021) Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. Inf Sci 548:295\u2013312","journal-title":"Inf Sci"},{"issue":"3","key":"4950_CR23","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 Fuzzy Syst 40(3):4331\u20134343","journal-title":"J Intell Fuzzy Syst"},{"key":"4950_CR24","doi-asserted-by":"crossref","unstructured":"Lin R, Fu C, Mao C, Wei J, Li J (2018) Academic news text classification model based on attention mechanism and RCNN. In: CCF Conference on Computer Supported Cooperative Work and Social Computing, Springer, Singapore, pp 507\u2013516","DOI":"10.1007\/978-981-13-3044-5_38"},{"key":"4950_CR25","doi-asserted-by":"crossref","unstructured":"Tang Q, Chen J, Lu H, Du Y, Yang K (2019) Full attention-based bi-GRU neural network for news text classification. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), IEEE pp 1970\u20131974","DOI":"10.1109\/ICCC47050.2019.9064061"},{"key":"4950_CR26","doi-asserted-by":"crossref","unstructured":"Duan J, Zhao H, Qin W, Qiu M, Liu M (2020) News text classification based on MLCNN and BiGRU hybrid neural network. In: 2020 3rd International Conference on Smart BlockChain (SmartBlock), IEEE pp 1\u20136","DOI":"10.1109\/SmartBlock52591.2020.00032"},{"key":"4950_CR27","doi-asserted-by":"crossref","unstructured":"Ruan J, Caballero JM, Juanatas RA (2022) Chinese news text classification method based on attention mechanism. In: 2022 7th International Conference on Business and Industrial Research (ICBIR), IEEE pp 330\u2013334","DOI":"10.1109\/ICBIR54589.2022.9786458"},{"issue":"2","key":"4950_CR28","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s10462-021-09993-z","volume":"55","author":"T Huang","year":"2022","unstructured":"Huang T, Zhang Q, Tang X, Zhao S, Lu X (2022) A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems. Artif Intell Rev 55(2):1289\u20131315","journal-title":"Artif Intell Rev"},{"key":"4950_CR29","doi-asserted-by":"crossref","unstructured":"Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v29i1.9513"},{"issue":"1","key":"4950_CR30","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1049\/cje.2018.11.004","volume":"28","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Zheng J, Jiang Y, Huang G, Chen R (2019) A text sentiment classification modeling method based on coordinated CNN-LSTM-attention model. Chin J Electron 28(1):120\u2013126","journal-title":"Chin J Electron"},{"key":"4950_CR31","doi-asserted-by":"crossref","unstructured":"Zheng S, Yang M (2019) A new method of improving BERT for text classification. In: International Conference on Intelligent Science and Big Data Engineering, Springer, Cham, pp 442\u2013452","DOI":"10.1007\/978-3-030-36204-1_37"},{"key":"4950_CR32","doi-asserted-by":"crossref","unstructured":"Khandve SI, Wagh VK, Wani AD, Joshi IM, Joshi RB (2022) Hierarchical neural network approaches for long document classification. In: 2022 14th International Conference on Machine Learning and Computing (ICMLC) pp 115\u2013119","DOI":"10.1145\/3529836.3529935"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04950-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-022-04950-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04950-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T19:20:18Z","timestamp":1677784818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-022-04950-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,19]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["4950"],"URL":"https:\/\/doi.org\/10.1007\/s11227-022-04950-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,19]]},"assertion":[{"value":"9 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2022","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 author declares that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}