Research on text classification of telecom user query

Text classification plays a more and more important role in many practical applications. However, there are a large number of unevenly distributed but important data about the complaints of Chinese telecom users. How to efficiently and accurately implement these telephone consultation contents with only a small number of samples to specific departments and improve the user experience has become an urgent problem to be solved. In this paper, we propose a multi modelbased machine learning method—telecom textCNN predictor which can effectively improve the accuracy and recall of the model on the long tail data, and automatically mark the text, so as to accurately locate the class of the problem. The results show that our model is helpful for business personnel to quickly identify the type of complaint business, conduct targeted business acceptance and user services, and improve user perception and produces better accuracy than the SVM model.