{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:55:01Z","timestamp":1781690101044,"version":"3.54.5"},"reference-count":53,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52174184"],"award-info":[{"award-number":["52174184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Short text classification remains a challenging task due to semantic sparsity, limited contextual information and the susceptibility of graph\u2010based encodings to structural noise. Existing graph neural network and contrastive learning approaches partially mitigate these issues. They enrich contextual representations through neighbourhood aggregation and reduce structural noise through consistency constraints on representations. However, they still struggle to model heterogeneous semantic relations and often produce unstable multiview representations. To address these limitations, this paper proposes an efficient short text classification model based on Feature Shuffling and Attention\u2010enhanced Multiview Contrastive Learning (FSAMCL). The method constructs heterogeneous graphs incorporating words, entities and part\u2010of\u2010speech tags, which are encoded using graph convolutional networks, while a term\u2013document matrix models global semantic distributions. Feature\u2010wise attention is applied to recalibrate node representations by amplifying salient semantic dimensions and suppressing irrelevant features. To enhance robustness, feature shuffling introduces lightweight, semantically coherent perturbations and singular value decomposition\u2013based noise decomposition extracts low\u2010rank semantic structures to generate noise\u2010resistant augmented views. Cross\u2010view attention then aligns and selectively integrates complementary information across heterogeneous views. Leveraging these enhanced representations, the framework jointly optimizes three contrastive objectives that reinforce invariance to perturbations, improve semantic discriminability and enhance cross\u2010view consistency. Experiments on four benchmark datasets demonstrate the effectiveness of FSAMCL, with the largest performance gain observed on the Twitter dataset, where the model surpasses the previous best method by 3.09% in accuracy and 3.27% in F1 score. These findings highlight the contribution of FSAMCL as a robust, noise\u2010tolerant and semantically enriched solution for short text classification.<\/jats:p>","DOI":"10.1111\/exsy.70312","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:35:47Z","timestamp":1779960947000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Short Text Classification Model Based on Feature Shuffling and Attention\u2010Enhanced With Multiview Contrastive Learning"],"prefix":"10.1111","volume":"43","author":[{"given":"Fangbo","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software Liaoning Technical University  Huludao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-1667","authenticated-orcid":false,"given":"Yongqing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Software Liaoning Technical University  Huludao China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2022.03.020"},{"key":"e_1_2_11_3_1","unstructured":"Chen J. 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