Study On The Evolution Of Event-Centered Online Public Opinion

With the development of social media,the growing enthusiasm of Internet users' participation in the discussion and spread of public events has embolden the impact of public opinion on real society.On the one hand,under the influence of the network clustering effect,the event-centered online public opinion is prone to the polarization of opinion and emotional aggregation,which leads to serious realistic consequences.On the other hand,the opinions and suggestions expressed by the public in the social media can facilitate the handling of the event and help the government to respond accurately to the users' doubts and dissatisfaction.Media coverage and public concern in social media have jointly promoted the dynamic change of public opinion.However,the traditional online public opinion analysis is often concerned only with the evolution of public opinion in social media,while ignoring the development of event itself.Moreover,with the increasing amount of internet text data,content analysis based on sample extraction limits our interpretation of public opinion evolution.In the above background,based on agenda setting theory and text mining,this paper first established an event-centered network public opinion analysis framework.Then,we extend to event layer,topic layer and view layer and through the empirical case respectively explored the different levels of their own evolution process.Finally,the interaction between the media agenda represented by the event layer and the public agenda represented by the topic layer and the view layer is studied from the two aspects of the issue and the attribute.The main research work and conclusions are as follows:Firstly,the method of extracting the event storylines and interaction from the news report collection is improved.The event correlation degree is calculated by using time coherence,subject affinity and sub-topic consistency,and event-related network is established through case event data,and the evolution pattern of event plot is analyzed.The study finds that the extraction method of event storyline interaction structure can clearly show the complete event context and help users and policy makers to better understand and grasp the development of event reports.At the same time,the news media will repeat the report to strengthen the public awareness of certain sub-events,but there is agenda competition between news platforms.Secondly,the dynamic topic model is used to analyze the microblogging text related to the event,and the main subtopics in the process of public opinion evolution are extracted.From the content of the subtopics,most of the discussions focus on the event progress and full of humanistic.Viewing from the keyword changes,the government's positive attitude response to the event can greatly ease the deterioration of public opinion,and try to delay the report or negative reaction is not conducive to the guidance of public opinion.Thirdly,we propose a method using lexical annotation and syntactic analysis to extract the microblog opinion orientation of the event entities,and use the word cloud to show the microblogging view portraits.This paper puts forward the method of extending the emotional dictionary by using the Word2 Vec,and analyzes the evolution of the public emotion with the emotional polarity and sentiment categories.The results show that "puzzles","concerns" and "slander" are often accompanied by public safety events and dominate the public opinion.Meanwhile,"doubts" will lengthen the life cycle of public opinion,playing a leading role in provoking controversy.Fourthly,the Granger causality analysis is used to examine the interaction between the media agenda and the public agenda.This paper also explores the relationship between the news reports and the emotions expressed in microblogs.It is found that the media agenda influences the issues of the public agenda,while the public agenda affects the issue attribute of the media agenda.At the same time,in the "Qing'an shooting incident" situation,the emotional words used in the news reports and the emotional types expressed in microblogs has a significant positive correlation.The main improvements and innovations of this study are the following three aspects:Firstly,the news data and social media data related to the event are integrated into the evolution analysis of the network public opinion.Under the guidance of the agenda setting theory,the content level of the traditional public opinion analysis is extended to the event layer,the topic layer and the opinion layer.The agenda setting analysis is extended by transferring the issue level to the attribute level which improved the public opinion framework.Secondly,this paper improves the way to dynamically track events and uses the community detection method to automatically extract the focusing sub-event.The dynamic topic model is introduced to extract the microblogging topic,and the evolution of the topic is explored from two levels: the topic popularity and the keywords.We also put forward the method of microblogging opinion portraits and use Word2 Vec to expand the sentiment dictionary.These works make up for the lack of network public opinion evolution analysis.Thirdly,it explores the interactive relationship between different levels of network public opinion under different event contexts,which provides a theoretical basis for future analysis of public opinion evolution,cluster behavior and sentiment communication.