Classification Of News Text Based On Deep Learning And Its Application

News text information is a kind of text information which is widely contacted in our life,and it is also one of the important means for people to understand social development.On the one hand,people can browse news text information,when looking for news categories they are interested in,they may be mixed with other categories.Using text classification technology to correctly classify news text can save users time in obtaining information.On the other hand,Internet companies can classify news texts,put different categories in different category databases,and automate recommendations based on user needs,saving manpower and resources and improving efficiency.Deep learning methods are more and more widely used in the field of natural language processing.This paper makes use of the strong advantages of deep learning and applies it to the field of news text classification.The specific content includes:(1)The basic process of text classification is reviewed,and the commonly used text classification algorithms,including traditional machine learning and deep learning algorithms,are analyzed.(2)Aiming at the problem that feature engineering of traditional machine learning algorithms,including text preprocessing,text representation,feature selection and other steps,which greatly increases the classification workload;this paper proposes a deep learning model of Text CNN based on attention mechanism.Firstly,using Text CNN as the basic classification model.Then,using multi-scale convolution kernel,using 2-max pooling instead of max pooling in pooling layer and adding the average pooling.Finally,add the attention mechanism after the pooling layer,refine the characteristics of the deep feature text obtained by the pooling of 2-max pooling and average pooling,and give two kinds of weight distribution after the pooling.Focus on information that is more representative of text features,thereby improving the classification efficiency.(3)Aiming at the problem that Text CNN model pays less attention to the context information in the classification.Firstly,LSTM is selected as the news textclassification model,LSTM model can process the context feature information through the state information of the hidden layer.Then,through the fusion of Attention-Text CNN and LSTM,to a certain extent,the two advantages are combined to build the news classification model of the fusion of Attention-Text CNN and LSTM.Finally,in the news text data set,through the comparative experiment,it can be seen that the accuracy of the fusion model is higher than that of the Attention-Text CNN and LSTM models before fusion,and finally reaches 97.81%.(4)Design and implement a web-based news classification system and embed the fusion model into the news classification system.The system inputs the news text information in many ways.The news text information is classified in the fusion model of Attention-Text CNN and LSTM,and the correct classification results are obtained,and the results are visualized.After the test,the realization of each module meets the requirements of the system design.