Abstract
This paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.
Highlights
With the rapid development of internet technology and mobile communication technology, online news has become one of the important information sources for people’s daily life, study, and work [1]
As one of the core technologies of information resource organization and management, text classification can solve the problem of information clutter to a large extent, help users narrow the scope of information retrieval, and make users more convenient and efficient to filter through the massive information resources to meet their own needs. e information is a powerful means to deal with massive information resources [4]
The news text is input into the convolutional neural network in the form of a twodimensional matrix, where u represents the result of encoding, and u is decoded in the decoding module, and the convolution operation is used to extract the features of the news text. e convolution operation of the convolutional neural network is as follows: w(x) max{0, 1 − u(x) + v(x · s(x))}. (4)
Summary
News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model. En, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. Experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. is article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted
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