{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T06:58:13Z","timestamp":1769929093669,"version":"3.49.0"},"reference-count":33,"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\/501100003459","name":"Guizhou University Talent Introduction","doi-asserted-by":"publisher","award":["2015-12"],"award-info":[{"award-number":["2015-12"]}],"id":[{"id":"10.13039\/501100003459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003459","name":"Guizhou Science and Technology Plan Project","doi-asserted-by":"publisher","award":["7429"],"award-info":[{"award-number":["7429"]}],"id":[{"id":"10.13039\/501100003459","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3094026","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T19:49:17Z","timestamp":1625168957000},"page":"94748-94755","source":"Crossref","is-referenced-by-count":13,"title":["ALBERTC-CNN Based Aspect Level Sentiment Analysis"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-1577","authenticated-orcid":false,"given":"Xingxin","family":"Ye","sequence":"first","affiliation":[{"name":"College of Big Data Information Engineering, Guizhou University, Guiyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2758-1880","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Big Data Information Engineering, Guizhou University, Guiyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4708-5262","authenticated-orcid":false,"given":"Mengshi","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Big Data Information Engineering, Guizhou University, Guiyang, China"}]}],"member":"263","reference":[{"key":"ref33","article-title":"CvT: Introducing convolutions to vision transformers","author":"wu","year":"2021","journal-title":"arXiv 2103 15808"},{"key":"ref32","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"arXiv 1706 03762"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019277"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1464"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1047"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1234"},{"key":"ref13","author":"radford","year":"2018","journal-title":"Improving language understanding by generative pre-training"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref15","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proc North Amer Chapter Assoc Comput Linguistics Hum Lang Technol"},{"key":"ref16","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"liu","year":"2019","journal-title":"arXiv 1907 11692"},{"key":"ref17","article-title":"ALBERT: A lite BERT for self-supervised learning of language representations","author":"lan","year":"2019","journal-title":"arXiv 1909 11942"},{"key":"ref18","first-page":"187","article-title":"Aspect-based sentiment analysis using BERT","author":"hoang","year":"2019","journal-title":"Proc NODALIDA"},{"key":"ref19","article-title":"Lite transformer with long-short range attention","author":"wu","year":"2020","journal-title":"arXiv 2004 11886"},{"key":"ref28","first-page":"197","article-title":"Aspect level sentiment classification with attention-over-attention neural networks","author":"huang","year":"2018","journal-title":"Proc Int Conf Social Comput Behav -Cultural Modeling Predict Behav Represent Modeling Simulation"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1063\/1.4994463","article-title":"Aspect-based sentiment analysis to review products using Naïve Bayes","volume":"1867","author":"mubarok","year":"2017","journal-title":"Proc AIP Conf"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1087"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1049"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1380"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTM.2017.8089134"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007465528199"},{"key":"ref7","first-page":"1081","article-title":"Kernel logistic regression and the import vector machine","author":"zhu","year":"2001","journal-title":"Proc Int Conf Neural Inf Process Syst Natural Synth"},{"key":"ref2","first-page":"1","article-title":"Effective method for sentiment lexical dictionary enrichment based on Word2 Vec for sentiment analysis","author":"alshari","year":"2018","journal-title":"Proc 4th Int Conf Inf Retr Knowl Manage (CAMP)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref1","article-title":"Aspect—Based sentiment analysis using context-aware learning for online reviews","author":"mahalakshmi","year":"2016","journal-title":"Proc Int Conf Innov Eng Technol (ICIET)"},{"key":"ref20","article-title":"Gaussian error linear units (GELUs)","author":"hendrycks","year":"2016","journal-title":"arXiv 1606 08415"},{"key":"ref22","article-title":"Effective LSTMs for target-dependent sentiment classification","author":"tang","year":"2015","journal-title":"arXiv 1512 01100"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2076"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1058"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186001"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/568"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09469770.pdf?arnumber=9469770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T20:48:45Z","timestamp":1662670125000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9469770\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3094026","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}