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A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

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Title: A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
Authors: Qiao, Zhilei
Zhang, Xuan
Zhou, Mi
Wang, Gang Alan
Fan, Weiguo
Keywords: Product defects
online customer reviews
text analysis
product quality
Issue Date: 04 Jan 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.
Pages/Duration: 10 pages
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.222
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Data Analytics and Data Mining for Social Media Minitrack

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