Abstract
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers' attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation of sample data. Firstly, this paper analyzes the model structure of a typical convolutional neural network model to increase the network depth and width in order to improve its performance, analyzes the network structure that further improves the model performance by using the attention mechanism, and then summarizes and analyzes the current special model structure. In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of text features and language knowledge are proposed. The text features and language knowledge are integrated into the language processing model, and the accuracy of the text language processing model is improved by parameter optimization. Experimental results on data sets show that the accuracy of the proposed model reaches 93.0%, which is better than the reference model in the literature.
Highlights
Text language processing is widely used in the fields of network public opinion, crisis public relations, brand marketing, and so on
In order to improve the accuracy of network text language processing and study the role of language knowledge and affective knowledge in the model, this paper proposes a convolutional neural network model based on the fusion of text features and language knowledge, which integrates words, parts of speech, effective dictionaries, and other external knowledge into the language processing model
The word vector training model is used to train the word vector, and the part of speech and affective words are added to produce a variety of feature data, which is used to eliminate word ambiguity and express emotional information. en, the convolutional neural network model was constructed, and various features were fused into the model
Summary
Text language processing is widely used in the fields of network public opinion, crisis public relations, brand marketing, and so on. E neural network model has been widely used in the fields of image processing, speech recognition, and text analysis and has achieved better results than traditional machine learning methods. A multiattention convolutional neural network model is proposed for a specific target emotion analysis task, in which three kinds of attention feature matrices are used: word, part of speech, and word position. A multichannel convolutional neural network model is proposed for the sentiment analysis task of Chinese microblogs, which integrates multiple emotion information features such as words, part of speech, and word position [13].
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