Pareto charting using multifield freestyle text data applied to Toyota Camry user reviews

被引:5
作者
Allen, Theodore T. [1 ]
Xiong, Hui [1 ]
机构
[1] Ohio State Univ, Columbus, OH 43210 USA
关键词
topic model; information retrieval; quality control; unsupervised learning; MODEL;
D O I
10.1002/asmb.947
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
This article proposes a method for Pareto charting that is based on unsupervised, freestyle text such as customer complaint, rework, scrap, or maintenance event descriptions. The proposed procedure is based on a slight extension of the latent Dirichlet allocation method to form multifield latent Dirichlet allocation. The extension is the usage of field-specific dictionaries for multifield databases and changes to recommended default prior settings. We use a numerical study to motivate the prior setting selection. A real-world case study associated with user reviews of Toyota Camry vehicles is used to illustrate the practical value of the proposed methods. The results indicate that only 4% of the words written by Consumer Reports reviewers from the last 10 years relate to the widely publicized unintended acceleration issue. Copyright (c) 2012 John Wiley & Sons, Ltd.
引用
收藏
页码:152 / 163
页数:12
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