| At present,with the rapid of development of micro-blog.It has become one of the main platforms for communication betweem netizens.Netizens are the main users of micro-blog.According to the research of the properties and behaviors of netizens,scholars found that a small amount of core netizens play a criticial role in the propagation of information in micro-blog.Key netizens in micro-blog have great influences.Therefore,the research on influence analysis and evolutionary trend prediction of netizens is an important research topic in the fields of network public opinion monitoring,intelligrnt information processing and information security,which has theoretical and practical value.This paper focuses on how to analyze influence and predict evolutionary trend of netizens.The problem of influence analysis of netizens is to calculate and sort the influence of netizens to other users in micro-blog.This paper proposes and implements a method of netizens influence ranking based on IntegrationRanker,and a method of netizens influence ranking based on learing to rank.This method based on IntegrationRanker has two improvements based on classical PageRank algorithm.The first improvement is to introduce the following netizens features to calculate initial PageRank: users activity,fans approval and so on.The second improvement is to change the allocation way in the iterative process of the PageRank algorithm.The second method based on learing to rank transforms the problem of calculating netizens influence into a ranking problem.This method uses the algorithm of learning to rank to train ranking model.The problem of evolutionary trend prediction for netizens is to predict evolutionary trend of netizens based on existing features of netizens to predict the future features.This paper proposes and implements a method of evolutionary trend prediction based on time horizon,and a method of evolutionary trend prediction based on topic.The key idea of the two methods is to transform the problem of user influence prediction into the classification problem.These methods use classification models to predict influence ranking of netizens in the future time period or unknown topic areas.These two methods transform the prediction problem of user influence into the TOP-K prediction problem.Time datasets and topic datasets are used to test our proposed algorithms in this paper.Experimental results show that the performance of method of netizens influence ranking based on IntegrationRanker is better than that of the classical methods of netizens influence.The performance of the method of netizens influence ranking based on learing to rank is better when using random forest model than using MART,RankNet and RankBoost models.The performance of the methods of evolutionary trend prediction based on time horizon and topic is better when using LibSVM model than using J48 decision tree,native Bias and random forest models.Experimental results show the methods which proposed by this paper is effective. |
