Abstract: Cars play an important role in many aspects of people's social life, and the effective handling of car quality complaints is of great significance to the proper running of cars and the reputation maintenance of car brands; effective classification of car quality complaint texts is the basis of the efficient handling of corresponding quality complaints, while relying on manual classification has disadvantages such as heavy workload, experience dependence, and error proneness; machine learning methods have been quite widely used in the automatic classification modeling for different types of natural language texts. It is of great practical significance to construct the automatic classification model of car quality complaints based on machine learning. Based on the characteristics of car quality complaint texts, this study vectorized the texts after word segmentation, performed feature selection and dimension reduction based on correlation analysis, and combined the progressive model training method and support vector machine to construct the classification model; in model reliability analysis, it was evaluated based on the effect of data amount on the modeling and the effect of text length on the prediction probability distribution. The results show that based on the method in this study, effective automatic classification model of car quality complaint texts could be constructed.

Chen Xiao Yu, Hou Xia, Zhang Xiao Min and Song Ying, “Modeling for Car Quality Complaint Classification based on Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130569

@article{Yu2022,
title = {Modeling for Car Quality Complaint Classification based on Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130569},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130569},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Chen Xiao Yu and Hou Xia and Zhang Xiao Min and Song Ying}
}