A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and <i>q</i> ‐rung orthopair fuzzy interaction Heronian mean operators

International Journal of Intelligent SystemsVolume 35, Issue 5 p. 783-825 RESEARCH ARTICLE A decision-making algorithm for online shopping using deep-learning–based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators Zaoli Yang, Zaoli Yang College of Economics and Management, Beijing University of Technology, Beijing, ChinaSearch for more papers by this authorTianxiong Ouyang, Tianxiong Ouyang School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorXiangling Fu, Corresponding Author Xiangling Fu fuxiangling@bupt.edu.cn School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China Correspondence Xiangling Fu, School of Software Engineering, Beijing University of Posts and Telecommunications, 100876 Beijing, China. Email: fuxiangling@bupt.edu.cnSearch for more papers by this authorXindong Peng, Xindong Peng Department of Software Engineering, School of Information Sciences and Engineering, Shaoguan University, Shaoguan, ChinaSearch for more papers by this author Zaoli Yang, Zaoli Yang College of Economics and Management, Beijing University of Technology, Beijing, ChinaSearch for more papers by this authorTianxiong Ouyang, Tianxiong Ouyang School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorXiangling Fu, Corresponding Author Xiangling Fu fuxiangling@bupt.edu.cn School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China Correspondence Xiangling Fu, School of Software Engineering, Beijing University of Posts and Telecommunications, 100876 Beijing, China. Email: fuxiangling@bupt.edu.cnSearch for more papers by this authorXindong Peng, Xindong Peng Department of Software Engineering, School of Information Sciences and Engineering, Shaoguan University, Shaoguan, ChinaSearch for more papers by this author First published: 07 March 2020 https://doi.org/10.1002/int.22225Citations: 49Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract In the process of online shopping, consumers usually compare the review information of the same product in different e-commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep-learning–based opinion mining and q-rung orthopair fuzzy interaction weighted Heronian mean (q-ROFIWHM) operators. First, in the proposed method, the deep-learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute-opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross-decision matrices, which are represented by the q-rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q-ROFIWHM operators are proposed to aggregate this cross-decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority. CONFLICT OF INTERESTS The authors declared that there is no conflict of interests. Citing Literature Volume35, Issue5May 2020Pages 783-825 RelatedInformation