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Analyze The Influence Of Official Information On Network Public Opinion Based On Visualization Method

Posted on:2019-01-21Degree:MasterType:Thesis
Country:ChinaCandidate:C J DongFull Text:PDF
GTID:2428330593951071Subject:Software engineering
Abstract/Summary:PDF Full Text Request
In recent years,network public opinion,as a way of expressing views and emotions for the netizen,has attracted the attention of relevant departments and scholars both at home and abroad.What we focus on is how to guide the development of network public opinion in an effective way.In order to solve this problem,this paper presents a visual analysis system to analyze the development trend of network public opinion after the influence of official information.We use the topical events in micro-blog “THAAD in South Korea” as the case.Firstly,LDA topic model was used to extract the topics from relevant data on “THAAD in South Korea ",by analyzing the development of network public opinion according to the content of topics and analyzing people's attention degree about the event on the basis of concern degree of each topic.Then calculates the similarity among those topics,and also extract key events from the news about “THAAD in South Korea",adopting method of visualization and visual analysis to analyze official information on the network public opinion.Through our analysis in this paper,it can be found that the official information would spend 1 to 2 days on spreading widely,have a significant impact on the network public opinion within 2 to 4 days after release.The impact may put the relevant topic to a climax or push it down.During the process,the development tendency for the topic of high similarity is basically the same.This paper has provided relevant departments suggestions on guidance and adjustment of network public opinion for reference.If relevant departments hope to guide and regulate network public opinion via official information,it is much better to take measures 2 to 4 days in advance.
Keywords/Search Tags:Public Network Opinion, Visualization and Visual Analysis, LDA Topic Model, Topic Relation
PDF Full Text Request
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