针对论坛数据特点的汽车质量问题挖掘

doi:10.16381/j.cnki.issn1003-207x.2019.0233

Abstract

Abstract: As the embodiment of the core competitiveness of automobile manufacturers, automobile quality is the basis and guarantee for the development of automobile manufacturers in the market. Understanding and mastering the automobile quality problems from user feedback is an important means to maintain brand reputation, enhance market competitiveness, and be close to users.Based on the data of online forums, the car quality problems found by the users when using or driving the cars are excavated. According to the characteristics of the forum data and user experience,firstly, the text features are selected to identify the texts related to automobile quality problems in the user experience. Then, according to the relationship between automobile units corresponding to quality problems and the types of problems,a method is proposed to extract automobile quality problems.The Apriori algorithm is used to extract the automobile units, and the semantic K-means clustering and hierarchical clustering algorithm are used to extract the corresponding problem types. The combination of automobile units and the types of problems leads to the quality problems of automobiles.Finally, the feasibility and effectiveness of this method are verified by actual forum data. The proposed method to mine automobile quality problems based on the characteristics of forum datacan help automobile manufacturers obtain and analyze potential automobile quality problems in time and assist companiesin making management decisions, which is of great significance in the process of quality management.

Key words: forum data, user-generated content, automobile quality, quality problems mining, text mining

CLC Number:

F253.3

WANG Yu-hang, DANG Yan-zhong, XU Zhao-guang. Mining Automobile Quality Problems Based on the Characteristics of Forum Data[J]. Chinese Journal of Management Science, 2021, 29(9): 201-212.

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Mining Automobile Quality Problems Based on the Characteristics of Forum Data

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