A novel probabilistic graphic model to detect product defects from social media data

摘要

Product defects are a major concern for manufacturers and customers. Detecting product defects is vital for manufacturers to prevent enormous product failure costs. As the surge of social media is in vogue, social media data become an important information source for manufacturers to collect defect information. In this study, we propose a novel probabilistic graphic model to discover defects from social media data. We first use three filters, namely, sentiment filter, component-symptom filter and similarity filter, to select informative data. Second, we analyze the remaining data via the proposed probabilistic graphic model and identify defect-related data. Our method provides detailed defect information including defect types, defective components and defect symptoms which is omitted by previous research. A case study in the automobile industry validates the effectiveness and superior performance of our method compared to prior approaches.