A Novel Web Text Classification Model Based on SAS for e-commerce

In this paper, we establish a model to analysis business enterprise customer query information for text classification to help e-commerce companies control the user's spending habits, and help users to find their needed goods. This study accesses to customer inquiry data and preprocesses these text data firstly. Then, it applies the improved TF-IDF principle to obtain the text feature vectors. Finally, this study establishes the classification model combining the Naive Bayes text classification and the semi-supervised EM iterative algorithm and uses various criteria to evaluate the model. When facing multi-class text classification feature selection, keyword weights prone to great volatility. This study improves the keyword weight calculation formula to perfect the classification results. The experimental results show that classification has good classification effect.