基于社交媒体的汽车缺陷识别方法

doi:10.3969/j.issn.1007-7375.2018.06.005

工业工程 ›› 2018, Vol. 21 ›› Issue (6): 31-39.doi: 10.3969/j.issn.1007-7375.2018.06.005

基于社交媒体的汽车缺陷识别方法

王海杰, 吴琼

天津大学管理与经济学部, 天津 300072

收稿日期:2018-07-11 出版日期:2018-12-30 发布日期:2018-12-29
作者简介:王海杰(1993-)，男，江苏省人，硕士研究生，主要研究方向为文本挖掘和缺陷识别
基金资助:
国家自然科学基金资助项目（71472132）

A Defect Discovery Method of Vehicle Based on Social Media

WANG Haijie, WU Qiong

College of Management and Economics, Tianjin University, Tianjin 300072, China

Received:2018-07-11 Online:2018-12-30 Published:2018-12-29

摘要/Abstract

摘要： 由于社交媒体上的信息传播具有广泛性和快速性，及时从社交媒体中挖掘汽车的缺陷信息对汽车厂商改进产品设计、优化质量管理具有指导意义。目前有关社交媒体缺陷识别的研究挖掘的缺陷信息较少且方法以聚类为主，效果不是很好。在现有关于社交媒体缺陷识别研究的基础上，结合企业的实际需求，扩展了具体的缺陷类别；基于朴素贝叶斯的分类方法详细对比了3种特征提取方法，并在此基础上结合EM算法实现了半监督的分类学习。实验结果表明，在缺陷类别划分符合企业实际需求的情况下，所提出的方法能够有效地识别出对应类别的缺陷，为企业缺陷管理提供决策支持，同时可以降低一半的人工标注成本。

关键词: 社交媒体, 缺陷识别, 特征提取, 半监督学习

Abstract: For the widespread and rapid information dissemination on social media, it is of great significance for automobile manufacturers to improve the product design and quality management. The current research on social media defect discovery can only mine a little part of valuable information and the approach is mainly clustering. Based on the existing research of defect discovery from social media, defect categories are extended according to the actual situation of the company; three feature selection methods are compared by Naïve Bayes classification method, and on the basis of this, a semi-supervised learning method using EM is applied. The experimental results show that the method proposed can effectively detect the vehicle defects to provide decision support for the defect management of the enterprise and reduce half the cost of manual tagging.

Key words: social media, defect discovery, feature selection, semi-supervised learning

中图分类号:

F203

王海杰, 吴琼. 基于社交媒体的汽车缺陷识别方法[J]. 工业工程, 2018, 21(6): 31-39.

WANG Haijie, WU Qiong. A Defect Discovery Method of Vehicle Based on Social Media[J]. Industrial Engineering Journal , 2018, 21(6): 31-39.

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基于社交媒体的汽车缺陷识别方法

A Defect Discovery Method of Vehicle Based on Social Media

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