An Integrated Text Analytic Framework for Product Defect Discovery

TLDR

The explosion of social media has produced vast user‑generated content, yet research rarely examines this text for quality‑management insights. The study synthesizes prior text‑mining research and proposes an integrated framework for discovering product defects. The framework extracts signal cues from social media, feeds them into principal component analysis and logistic regression models, and applies this multivariate analysis to identify defects in automotive and consumer electronics products. Distinctive terms, product features, and semantic factors strongly indicate defects, while stylistic, social, and sentiment features do not, and for high‑sales products, faster defect discovery yields substantial corporate value.

Abstract

The recent surge in the usage of social media has created an enormous amount of user‐generated content (UGC). While there are streams of research that seek to mine UGC, these research studies seldom tackle analysis of this textual content from a quality management perspective. In this study, we synthesize existing research studies on text mining and propose an integrated text analytic framework for product defect discovery. The framework effectively leverages rich social media content and quantifies the text using various automatically extracted signal cues. These extracted signal cues can then be used as modeling inputs for product defect discovery. We showcase the usefulness of the framework by performing product defect discovery using UGC in both the automotive and the consumer electronics domains. We use principal component analysis and logistic regression to produce a multivariate explanatory analysis relating defects to quantitative measures derived from text. For our samples, we find that a selection of distinctive terms, product features, and semantic factors are strong indicators of defects, whereas stylistic, social, and sentiment features are not. For high sales volume products, we demonstrate that significant corporate value is derivable from a reduction in defect discovery time and consequently defective product units in circulation.