Identifying product defects from user complaints: A probabilistic defect model

Xuan Zhang, Lijie Tang, Edward Fox, Zhilei Qiao, Weiguo Fan, G. Alan Wang

Research output: Contribution to conferencePaper

Abstract

The recent surge in using social media has created a massive amount of unstructured textual complaints about products and services. However, discovering potential product defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical product issues and corresponding product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., product model, year of production, defective components, symptoms, etc.) of a product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers.

Original languageEnglish (US)
StatePublished - Jan 1 2016
Externally publishedYes
Event22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States
Duration: Aug 11 2016Aug 14 2016

Other

Other22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016
CountryUnited States
CitySan Diego
Period8/11/168/14/16

Keywords

  • Defect discovery
  • EM
  • K-Means
  • Opinion mining
  • Probabilistic defect model
  • Social media

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Zhang, X., Tang, L., Fox, E., Qiao, Z., Fan, W., & Wang, G. A. (2016). Identifying product defects from user complaints: A probabilistic defect model. Paper presented at 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016, San Diego, United States.