A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets Basiri, ME, Nemati, S, Abdar, Moloud, Asadi, S and Acharrya, UR 2021, A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets, Knowledge-Based Systems, vol. 228, pp. 1-21, doi: 10.1016/j.knosys.2021.107242. Attached Files Name Description MIMEType Size Downloads Title A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets Author(s) Basiri, ME Nemati, S Abdar, Moloud orcid.org/0000-0002-3059-6357 Asadi, S Acharrya, UR Journal name Knowledge-Based Systems Volume number 228 Article ID 107242 Start page 1 End page 21 Total pages 21 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021 ISSN 0950-7051 1872-7409 Keyword(s) CLASSIFICATION Computer Science Computer Science, Artificial Intelligence Coronavirus (COVID-19) Deep learning EVENT DETECTION Information fusion Science & Technology Sentiment analysis Technology Tweet analysis TWITTER DATA Language eng DOI 10.1016/j.knosys.2021.107242 Field of Research 08 Information and Computing Sciences 15 Commerce, Management, Tourism and Services 17 Psychology and Cognitive Sciences HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30153032 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 02 Jul 2021, 08:13:09 EST Sat, 03 Jul 2021, 05:01:12 EST Mon, 05 Jul 2021, 08:06:13 EST Sat, 17 Jul 2021, 02:42:24 EST Sat, 18 Sep 2021, 02:30:34 EST Wed, 24 Nov 2021, 05:02:31 EST Wed, 12 Jan 2022, 13:31:17 EST Wed, 12 Jan 2022, 15:42:06 EST Wed, 12 Jan 2022, 15:46:12 EST Sun, 16 Jan 2022, 23:36:00 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 16 times in Scopus Search Google Scholar Access Statistics: 45 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 02 Jul 2021, 08:13:09 EST