Identifying helpful quality-related reviews from social media based on attractive quality theory. (17th November 2019)

• Record Type: Journal Article
• Authors: Liu, Yao; Jiang, Cuiqing; Ding, Yong; Wang, Zhao; Lv, Xiaozhong; Wang, Junhua
• Is Part Of: Total quality management & business excellence. Volume 30:Number 15/16(2019)
• Page Start: 1596
• Page End: 1615
• Publication Date: 2019-11-17
• Subjects: attractive quality theory -- total quality management -- multi-label classification -- imbalanced data classification; Total quality management -- Periodicals; Quality control -- Periodicals; Quality assurance -- Periodicals; Production management -- Quality control -- Periodicals; 658.401305
• DOI: 10.1080/14783363.2017.1389265
• Languages: English
• ISSNs: 1478-3363

Abstract

Abstract : Social media provides customers with platforms on which to express usage experiences, opinions, preferences and expectations of product quality. Thus, a large number of reviews available on social media become an effective information source for quality management. In this study, we attempt to identify reviews that are helpful from the perspective of total quality management, called Helpful Quality-related Reviews (HQRs). First, we propose a definition and taxonomy of HQRs based on attractive quality theory. Then, we construct a Helpful Quality-related Review Identification (HQRI) model to mitigate the information overload represented by social media reviews. The HQRI model incorporates an imbalanced data classification method and a multi-label classification method based on the characteristics of HQRs. Experimental results demonstrate the effectiveness of the HQRI model in terms of six performance metrics in comparison with three state-of-the-art methods. Finally, we employ the Latent Dirichlet Allocation (LDA) topic generation model and word clouds to analyse and present the topics, specific manifestations, customer behaviours and related product components mentioned in HQRs.