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

机译:使用应用于 Toyota Camry 用户评论的多字段自由式文本数据的帕累托图

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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.
机译:本文提出了一种基于无监督的自由式文本(如客户投诉、返工、报废或维护事件描述)的帕累托图方法。所提出的程序基于对潜在狄利克雷分配方法的轻微扩展,以形成多场潜在狄利克雷分配。该扩展是对多字段数据库使用特定于字段的词典,以及对建议的默认先前设置的更改。我们使用数值研究来激励先前的设置选择。本文通过与丰田凯美瑞汽车用户评论相关的真实案例研究来说明所提方法的实用价值。结果表明,在过去10年中,《消费者报告》评论员所写的文字中只有4%与广为人知的意外加速问题有关。

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