Conference proceeding
A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
Proceedings of the 50th Annual Hawaii International Conference on System Sciences, pp.1821-1830
01/01/2017
Abstract
Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products' defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners.
Details
- Title: Subtitle
- A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
- Creators
- Zhilei Qiao - Virginia TechXuan Zhang - Virginia TechMi Zhou - Virginia TechAlan Wang - Virginia TechWeiguo Fan - Virginia Tech
- Contributors
- T X Bui (Editor)R Sprague (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 50th Annual Hawaii International Conference on System Sciences, pp.1821-1830
- Publisher
- HICSS
- Number of pages
- 10
- Grant note
- 71531013 / Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) Center for Business Intelligence and Analytics (CIBA) at Virginia Tech
- Language
- English
- Date published
- 01/01/2017
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380426902771
Metrics
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