Abstract: With the rapid development of online shopping,a large amount of transaction data has been generated in online transaction activities between online merchants and shoppers,which contain great analytical value.Aiming at the text classification pro-blem of social e-commerce product texts,in order to more efficiently and accurately determine the category of products described in the text,this paper proposes a social e-commerce text classification algorithm based on BERT model.The algorithm adopts the BERT pre-trained language model to complete the feature vector representation of social e-commerce text on sentence-level,and then inputs the obtained feature vectors into the targeted classifier for classification.In this paper,we use the social e-commerce text data set for algorithm verification,and the results show that the F1 value of the trained model on the test set can reach up to 94.61%,which is 6% higher than the MRPC classification task based on the BERT model.Therefore,the social e-commerce text classification algorithm proposed in this paper can more efficiently and accurately determine the type of goods described in the text,which is helpful for further analysis of online transaction data and extraction of valuable information from massive data.

Key words: Bidirectional encoder, Feature extraction, Machine learning, Model building, Multi-label text classification