Research on the Classification Modeling for the Natural Language Texts with Subjectivity Characteristic

The methods of natural language text classification have the characteristic of diversification, and the text characteristics are the basis of the method effectiveness; this paper takes the car service complaint data as an example to study the classification modeling for the texts with subjectivity characteristic. The effective handling of car service complaints is important for improving user experience and maintaining brand reputation; manual classification commonly has the disadvantages of experience dependence, prone to error, heavy workload and so on; corresponding automatic classification modeling research is of great practical significance. The core links of the research method in this study include word segmentation, text vectorization, feature selection and dimensionality reduction based on correlation, classification modeling based on progressive method and random forest, and model reliability analysis; the research results show that the car service complaint texts could be effectively classified based on the method in this study, which could provide a reference for related further research and application.