Conference proceeding
Identifying Product Defects from User Complaints: A Probabilistic Defect Model
AMCIS 2016 PROCEEDINGS
01/01/2016
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.
Details
- Title: Subtitle
- Identifying Product Defects from User Complaints: A Probabilistic Defect Model
- Creators
- Xuan Zhang - Virginia TechZhilei Qiao - Virginia TechLijie Tang - Virginia TechWeiguo Fan - Virginia TechEdward Fox - Virginia TechG. Alan Wang - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- AMCIS 2016 PROCEEDINGS
- Publisher
- Assoc Information Systems
- Number of pages
- 10
- Language
- English
- Date published
- 01/01/2016
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380411502771
Metrics
3 Record Views