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Intelligent customer service platform has the ability of automatic communication and service based on natural language. It can achieve intelligent interaction between human and machine interaction, such as, providing users with higher quality services through instant messaging, Internet, telephone, text messaging, etc. This paper mainly explores the subdivision research of airline customers based on text classification. First of all, we extract the characteristics on text data of airline customer service platform through TF-IDF. Secondly, naive Bayes, SVM, KNN, and logistic regression are used to train the model. Thirdly, a combinational model based on the four algorithms is constructed; Finally, we use 10-fold cross validation to verify the testing results. And experiments show that the combinational algorithms are better than the original methods.
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