{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T04:10:06Z","timestamp":1748664606651,"version":"3.41.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Social Science Fund of China \u201cResearch on Methods for Mining Public Opinion Information on Emergency Events in Social Networks\u201d","award":["20CTJ011"],"award-info":[{"award-number":["20CTJ011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3572077","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T17:40:49Z","timestamp":1747849249000},"page":"90558-90571","source":"Crossref","is-referenced-by-count":0,"title":["An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3421-1565","authenticated-orcid":false,"given":"Xue","family":"Xu","sequence":"first","affiliation":[{"name":"College of Science, Tianjin University of Commerce, Tianjin, China"}]},{"given":"Haidong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Science, Tianjin University of Commerce, Tianjin, China"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Science, Tianjin University of Commerce, Tianjin, China"}]}],"member":"263","reference":[{"issue":"9","key":"ref1","first-page":"123","article-title":"Regional differences and emotional measurement of public opinion on public health events: Taking the COVID-19 epidemic as an example","volume":"40","author":"Zhang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103654"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/math10224255"},{"issue":"12","key":"ref4","first-page":"11","article-title":"A review of text sentiment analysis methods","volume":"57","author":"Wang","year":"2021","journal-title":"Comput. Eng. Appl."},{"issue":"8","key":"ref5","first-page":"109","article-title":"Fine-grained sentiment analysis combining financial domain sentiment lexicon and attention mechanism","volume":"36","author":"Zhu","year":"2022","journal-title":"J. Chin. Inf. Process."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/944012.944013"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"issue":"85","key":"ref10","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3214233"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105443"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICNLP55136.2022.00078"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"ref16","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/ijl\/3.4.235"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09030-1"},{"key":"ref19","first-page":"60","article-title":"Research on the construction method of domain sentiment lexicons","volume":"12","author":"Li","year":"2019","journal-title":"Library Theory Pract."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2016.11.001"},{"issue":"8","key":"ref21","first-page":"2231","article-title":"Adaptive learning method for Chinese domain sentiment lexicons","volume":"41","author":"Ye","year":"2020","journal-title":"Comput. Eng. Design"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.11.009"},{"issue":"3","key":"ref23","first-page":"324","article-title":"Construction of teaching evaluation text sentiment lexicon based on label propagation","volume":"50","author":"Ma","year":"2019","journal-title":"J. Inner Mongolia Univ., Natural Sci. Ed."},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1108\/INTR-11-2019-0461"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-022-10043-1"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1310.4546"},{"key":"ref27","first-page":"172","article-title":"Building large-scale twitter-specific sentiment lexicon: A representation learning approach","volume-title":"Proc. 25th Int. Conf. Comput. Linguistics, Tech. Papers","author":"Tang"},{"issue":"10","key":"ref28","first-page":"95","article-title":"Automatic construction of domain sentiment lexicons based on deep learning: A case study in the financial field","volume":"2","author":"Hu","year":"2018","journal-title":"Data Anal. Knowl. Discovery"},{"issue":"2","key":"ref29","first-page":"98","article-title":"Constructing a domain sentiment lexicon based on Chinese social media text","volume":"3","author":"Jiang","year":"2019","journal-title":"Data Anal. Knowl. Discovery"},{"issue":"12","key":"ref30","first-page":"3381","article-title":"Research on the construction of commodity price public opinion lexicon and public opinion index based on trend sentiment mapping","volume":"42","author":"Xu","year":"2022","journal-title":"Syst. Eng. Theory Pract."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106423"},{"issue":"140","key":"ref32","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-65175-5_20"},{"issue":"6","key":"ref34","first-page":"1660","article-title":"Construction method of sentiment lexicon for online travel reviews","volume":"36","author":"Yan","year":"2019","journal-title":"Appl. Res. Comput."},{"issue":"1","key":"ref35","first-page":"35","article-title":"Frontiers of graph neural networks and their applications","volume":"45","author":"Wu","year":"2022","journal-title":"Chin. J. Comput."},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref38","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014","journal-title":"arXiv:1409.3215"},{"key":"ref39","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11008636.pdf?arnumber=11008636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T17:48:21Z","timestamp":1748627301000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11008636\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3572077","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}