A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques Basiri, Mohammad Ehsan, Abdar, Moloud, Cifci, Mehmet Aakif, Nemati, Shahla and Acharya, U. Rajendra 2020, A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques, Knowledge-Based Systems, vol. 198, pp. 1-19, doi: 10.1016/j.knosys.2020.105949. Attached Files Name Description MIMEType Size Downloads Title A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques Author(s) Basiri, Mohammad Ehsan Abdar, Moloud orcid.org/0000-0002-3059-6357 Cifci, Mehmet Aakif Nemati, Shahla Acharya, U. Rajendra Journal name Knowledge-Based Systems Volume number 198 Article ID 105949 Start page 1 End page 19 Total pages 19 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2020-06-21 ISSN 0950-7051 Keyword(s) machine learning deep learning sentiment analysis information fusion 3-way decisions drug review Language eng DOI 10.1016/j.knosys.2020.105949 Indigenous content off Field of Research 08 Information and Computing Sciences 15 Commerce, Management, Tourism and Services 17 Psychology and Cognitive Sciences Persistent URL http://hdl.handle.net/10536/DRO/DU:30137759 Document type: Journal Article Collection: Deputy Vice-Chancellor Research Group 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 Mon, 01 Jun 2020, 12:57:05 EST Tue, 02 Jun 2020, 05:01:35 EST Thu, 20 Aug 2020, 06:02:20 EST Thu, 17 Sep 2020, 09:57:11 EST Thu, 17 Sep 2020, 10:00:31 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 11 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 01 Jun 2020, 12:57:05 EST

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.