Identifying helpful quality-related reviews from social media based on attractive quality theory

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.

Keywords:

• attractive quality theory
• total quality management
• multi-label classification
• imbalanced data classification

Additional information

Funding

This work was supported by National Natural Science Foundation of China [Grant Numbers 71731005, 71571059] Fund for Humanities and Social Science Fund Research Planning of the Ministry of Education [Grant Number 15YJA630010].