Research And Implementation Of Key Technologies Of MiscNews Analysis Based On Deep Learning

At present,the rapid development of information technology poses a huge challenge to news service.On the one hand,news media organizations need to obtain valuable news clues from massive media information;on the other hand,the majority of readers need to be liberated from reading long text,avoiding information overload while accurately obtaining news information.Therefore,advanced technologies such as text analysis and deep learning to realize intelligent news analysis.With the rapid development of technologies such as natural language processing,machine learning and deep learning,text analysis has changed from rule-based,dictionary based and statistical probability to intelligent analysis method that use machine learning and deep learning.Based on the deep learning algorithm and natural language processing,this paper research and analysis around five aspects of miscnews : news classification,news opinion extraction,news element extraction,news theme and news sentiment analysis.The goal is to achieve fast,accurate and convenient intelligent analysis of miscnews.The main research contents are as follows:(1)Through the study of text classification theory,combined with the characteristics of short news text with too long context dependence,a method of miscnews classification based on BERT-CNN model is proposed.Firstly,the character features of news text are extracted based on the BERT pre-trained Chinese language model,and then character vectors representing the features of news text are input to CNN to build miscnews classification model.(2)Aiming at the problems of diversified opinion and frequent the out-of-vocabulary words in miscnews,a method of opinion extraction based on BiLSTM-CRF model fusing character features is proposed.Method converting miscnews opinion extraction task to opinion tagging sequence annotation task,using character features input into the bidirectional long short-term memory network as observation sequence to calculate the labeling probability of each character,character features include character vectors,character parts of speech,relative position of characters and n-gram feature of character;finally,the optimal path of annotation sequence is computed by conditional random field to obtain the opinion.(3)By studying the theory and algorithm of fine-grained sentiment ananlysis,a finegrained sentiment analysis model of miscnews is constructed.First,the prediction of emotional polarity based on BERT-CNN model,and then extract miscnews elements based on BERT-BiLSTM-CRF model,and to weight the person and institutions in the news elements.At the same time,extract the opinion elements of miscnews and calculate the sentiment score of opinion sentiment words.Finally,combine with the sentiment polarity,sentitment scores and hierarchical weights for fine-grained sentiment analysis of miscnews.Finally,based on the relevant model of miscnews analysis,an intelligent news analysis system is designed and implemented which provides intelligent analysis of miscnews and a visual display of the analysis results.