LAUSR

Reviews posted to social media are an effective source of information for helping quality managers to improve product quality. However, because helpful quality-related reviews may involve various aspects of product quality, previous studies confusing these aspects cannot provide targeted information regarding different aspects of product quality and production system improvement. In this paper, we propose a method of multi-class classification for helpful quality-related reviews corresponding to different aspects of product quality and production systems. Furthermore, the efficient and accurate identification of helpful quality-related reviews remains a critical challenge because of the sparseness of such reviews, which significantly influences classifier performance. To address these problems, we develop a model for the identification of helpful reviews called Helpful Quality-related Review Mining (HQRM) that incorporates a multi-class classification architecture and imbalanced data classification methods. The experimental results show that HQRM enables the multi-class classification of helpful quality-related reviews with significantly improved precision, recall and F-measure values.