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Identifying Product Defects by Apply...

Fong, Titus Hei Yeung.

Identifying Product Defects by Applying a Predictive Model to Customer Reviews.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifying Product Defects by Applying a Predictive Model to Customer Reviews.
Author:	Fong, Titus Hei Yeung.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	146 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-03, Section: B.
Notes:	Advisor: Sarkani, Shahryar;Fossaceca, John.
Contained By:	Dissertations Abstracts International82-03B.
Subject:	Engineering.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28087840
ISBN:	9798662589445

Identifying Product Defects by Applying a Predictive Model to Customer Reviews.
Fong, Titus Hei Yeung.

Identifying Product Defects by Applying a Predictive Model to Customer Reviews. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 146 p.

Source: Dissertations Abstracts International, Volume: 82-03, Section: B.

Thesis (D.Engr.)--The George Washington University, 2020.

This item must not be sold to any third party vendors.

The challenge for consumer product engineering teams to manually explore their product’s defects from online customer reviews (OCR) delays product recall and recovery processes. In today's product life cycle, there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as design, performance, and serviceability feedback, to the product engineering teams. This lack of an early detection mechanism for problems often increases the risks of a product recall, potentially causing billions of dollars in economic loss, loss of company credibility, and loss of market penetration.This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide engineers with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews.Results of this praxis show that engineering teams can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach is able to locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the engineering team to take required mitigation actions earlier and proactively stop the diffusion of the detective products.

ISBN: 9798662589445Subjects--Topical Terms:

210888
Engineering.
Subjects--Index Terms:

Machine learning

Identifying Product Defects by Applying a Predictive Model to Customer Reviews.
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520 $a The challenge for consumer product engineering teams to manually explore their product’s defects from online customer reviews (OCR) delays product recall and recovery processes. In today's product life cycle, there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as design, performance, and serviceability feedback, to the product engineering teams. This lack of an early detection mechanism for problems often increases the risks of a product recall, potentially causing billions of dollars in economic loss, loss of company credibility, and loss of market penetration.This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide engineers with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews.Results of this praxis show that engineering teams can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach is able to locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the engineering team to take required mitigation actions earlier and proactively stop the diffusion of the detective products.
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