The spread of new coronavirus has a significant impact on the world. To protect people from viruses, a large number of personnel and substances in research, production, medical treatment, and other aspects are used to deal with the virus. News reporting is an important way for people to obtain information about the coronavirus. Objective and immediate news reports can provide people with accurate information and correct response methods, thus playing their due role in defeating the coronavirus. In order to compare the emotional characteristics of news reports, the methods of natural language processing are used to conduct sentiment analysis on news reports, including the subjectivity and the polarity of news reports. We select the news websites of BBC and CNN, that have an important influence in the world, for comparative analysis. The results show that BBC news reports are more positive than CNN, and in terms of subjective, BBC News is more factual than CNN.
This article mainly solves the problem of standardization of ownership and rights description of the main stakeholders in the field of data circulation in the field of data governance: combining the status quo of data protection and ownership identification, starting with the logical relationship between data and information, deconstructs the service data for subject ownership identification; combs the relevant laws and regulations of data protection, and defines the rights of each subject. In order to realize the structure and machine-readable of the right language, we should refer to the ODRL information model of W3C to build the right description standard of service data, describe the application of the standard in the actual transaction scenario, and build the hierarchical right transfer model in the process of data transaction.
Heart disease is an important disease that endangers human health, with a high mortality rate. Machine learning assisted diagnosis of medical data is a hot topic, and it has made great contributions in predicting patient outcomes and reducing mortality. Therefore, based on the heart disease index data, this paper uses Decision tree model, Clustering model, and Naive Bayes model to predict whether or not having heart disease. The results show that the Naive Bayes algorithm has better prediction accuracy and can assist doctors in diagnosis and treatment.
. This paper proposes the TransE-CBA (TransE Based On Improved Bernoulli And Adam) model, an improved TransE model. We used an improved Bernoulli distribution sampling method to improve the accuracy of negative samples, and used the Adam algorithm to update the gradient to improve the performance of the algorithm. Experiment result shows that, on the FB13 and WN18 datasets, the TransE-CBA model has a lower average ranking score (MeanRank) than TransE as a whole, and the proportion of the top ten triples (HITS@10) Higher, better sorting effect.
Clustering analysis is one of the hot research fields in data mining. EM algorithm is an effective method to realize maximum likelihood estimation, which is mainly used for parameter estimation of incomplete data. It greatly simplifies the likelihood function equation by assuming the existence of latent variable, while maximum likelihood estimation is a commonly used parameter estimation method, and the EM algorithm makes its application more extensive. This paper takes clustering algorithm as the main research object, introduces the basic idea of maximum likelihood estimation, describes the basic theory of EM algorithm, and realizes EM algorithm. The experimental results show that compared with the traditional clustering algorithm, the EM algorithm has better convergence and clustering ability.
