Application on text classification of telecom user complaints based on GRW and FastText model

With the widespread application of neural network, the application of neural network to natural language processing text classification problems has become an effective solution.The customer service center of telecom operator collected user complaint information from multiple channels.In order to automatically classify the complaint text information and assign it to the specific responsible department for processing and reply, enhancing customer perception further, a textclassification method based on GRW and FastTextmodel was proposed.Firstly, the GRW model was used to select the features of the complaint text, extract effective feature words, and then a user complaint text classification method based on FastText model was constructed.Experiments on public datasets and a complaint text data by one of telecom company show that the text classification method based on GRW and FastText model is better than naive Bayes, bidirectional LSTM and Bert pre-trained model in accuracy, Kappa coefficient and Hamming loss.