ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis Basiri, Mohammad Ehsan, Nemati, Shahla, Abdar, Moloud, Cambria, Erik and Acharrya, U. Rajendra 2021, ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis, Future Generation Computer Systems, vol. 115, pp. 279-294, doi: 10.1016/j.future.2020.08.005. Attached Files Name Description MIMEType Size Downloads Title ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis Author(s) Basiri, Mohammad Ehsan Nemati, Shahla Abdar, Moloud orcid.org/0000-0002-3059-6357 Cambria, Erik Acharrya, U. Rajendra Journal name Future Generation Computer Systems Volume number 115 Start page 279 End page 294 Total pages 16 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021-02 ISSN 0167-739X Keyword(s) Sentiment analysis Deep learning Convolutional neural network Long short-term memory Attention mechanism Language eng DOI 10.1016/j.future.2020.08.005 Indigenous content off Field of Research 0803 Computer Software 0805 Distributed Computing 0806 Information Systems HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30142027 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Institute for Intelligent Systems Research and Innovation (IISRI) Related Links Link Description Connect to published version 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. Versions Version Filter Type Wed, 16 Sep 2020, 15:03:32 EST Thu, 17 Sep 2020, 05:02:07 EST Thu, 17 Sep 2020, 07:31:20 EST Sat, 03 Oct 2020, 06:04:33 EST Tue, 06 Oct 2020, 08:21:55 EST Tue, 06 Oct 2020, 08:26:10 EST Tue, 06 Oct 2020, 08:27:23 EST Sat, 10 Oct 2020, 01:31:44 EST Sat, 16 Jan 2021, 01:31:08 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 58 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 16 Sep 2020, 15:03:32 EST

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