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 language | English (US) |
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State | Published - Jan 1 2016 |
Externally published | Yes |
Event | 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States Duration: Aug 11 2016 → Aug 14 2016 |
Other
Other | 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 |
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Country | United States |
City | San Diego |
Period | 8/11/16 → 8/14/16 |
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