基于引导式主题模型的产品缺陷信息识别方法及应用

Abstract

Extracting potential product defect information from the increasingly abundant online complaints has become a focal point and challenge in quality management for both manufacturers and regulatory agencies. To this end, this study proposes a novel product defect information detection model based on topic modeling. Firstly, the model takes advantage of the guided topic model to determine the number of defect topics through a defect dictionary, extract defect topics consistent with industry standards, and perform multi-label classification of complaints based on these topics. In addition, the model incorporates two improvement mechanisms, namely product feature modeling and adaptive term weighting, to further enhance defect recognition performance by addressing the unstructured nature of complaint information and the sparse distribution of defect words. This study collected 115,668 online complaints from a leading domestic automotive platform for experimentation. Experimental results indicate that compared to existing topic models such as latent Dirichlet allocation, the proposed model can generate defect topics consistent with actual industry defect classifications. Additionally, it can analyze the influence of product brand, energy type, and other features on the probability distribution of defect topics in complaints. The model outperforms other comparative topic models significantly in F1 score, AUC score, Hamming loss, and ranking loss. The product feature modeling, adaptive term weighting, and guided topic model structure of the model ensure its accuracy in all evaluation metrics. This research can help manufacturers and regulatory agencies monitor product quality levels in a timely manner and protect the legitimate rights and interests of consumers.