{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:58:04Z","timestamp":1780394284863,"version":"3.54.1"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of the Henan Province","doi-asserted-by":"publisher","award":["152102210068"],"award-info":[{"award-number":["152102210068"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2017YFD0401001"],"award-info":[{"award-number":["2017YFD0401001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3061450","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T20:57:53Z","timestamp":1614113873000},"page":"33410-33418","source":"Crossref","is-referenced-by-count":30,"title":["Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information"],"prefix":"10.1109","volume":"9","author":[{"given":"Minzheng","family":"Xuanyuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengshi","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"5439","DOI":"10.1007\/s11042-018-5748-4","article-title":"A novel approach to generate a large scale of supervised data for short text sentiment analysis","volume":"79","author":"sun","year":"2018","journal-title":"Multimedia Tools Appl"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102141"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05049-6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2349"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0064417"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2017.03.031"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref18","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"arXiv 1301 3781 [cs]"},{"key":"ref19","article-title":"Cyclical learning rates for training neural networks","author":"smith","year":"2015","journal-title":"arXiv 1506 01186"},{"key":"ref4","first-page":"1","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","author":"yang","year":"2019","journal-title":"Proc NIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1458"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.5573\/IEIESPC.2017.6.1.053"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref8","article-title":"A Bi-LSTM-RNN model for relation classification using low-cost sequence features","author":"li","year":"2016","journal-title":"arXiv 1608 07720"},{"key":"ref7","article-title":"Bag of tricks for efficient text classification","author":"joulin","year":"2016","journal-title":"arXiv 1607 01759"},{"key":"ref2","first-page":"5999","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc NIPS"},{"key":"ref1","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proc NAACL-HLT"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852406"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09360796.pdf?arnumber=9360796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:59Z","timestamp":1639771019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9360796\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3061450","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}