Product weakness finder: an opinion-aware system through sentiment analysis

Abstract

Purpose – Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way. Design/methodology/approach – PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations. Findings – The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy. Research limitations/implications – Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework. Practical implications – The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies. Social implications – A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare. Originality/value – An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.