Discussion on Big Data Network Public Opinion in Colleges and Universities

Online posts have gradually become a major carrier of network public opinion in social media, and the social network hotspots are the important basis for the study of network public opinion. Therefore, it is significant to extract hotspots for monitoring Internet public opinion from online posts textual big data. However, the current hotspot extraction methods are focused on the users’ features that are based on textual big data with spam and low-quality content. Meanwhile, these methods seldomly consider the time span of posts and the popularity of users. Accordingly, this article presents a hotspots information extraction hybrid solution of online posts’ textual data. Firstly, a filtering strategy to obtain more high-quality textual data is designed. Secondly, the topic hot degree is presented by considering the average number of replies and the popularity of the participant. Thirdly, an improved co-word analysis technology is used to search the same topic posts and Bisecting k-means clustering algorithm using repliers’ popularity and key posts are designed for studying and monitoring the hotspots of online posts in a valid big data environment. Finally, the proposed algorithms are verified in experiments by extracting the hotspots of online posts from the dataset. The results show that the data filtering strategy can help to obtain more valuable information and decrease the computing time. The results also demonstrate that the proposed solution can help to obtain hotspots comparing the traditional methods, and the hot degree can reflect the trend of the online post by comparing the traditional methods.

Download Full-text

Analysis of the Countermeasures of Dealing with the Network Public Opinions on Emergencies of Colleges in Gansu Province

Advances in Higher Education ◽

10.18686/ahe.v4i9.2618 ◽

2020 ◽

Vol 4 (9) ◽

Author(s):

Jianwen Duo ◽

Jin Wang ◽

Zhengyan Zhan ◽

Pinghui Yang

Keyword(s):

Public Opinion ◽

Colleges And Universities ◽

Gansu Province ◽

The Internet ◽

Public Attention ◽

Timely Manner ◽

Local Society ◽

Short Period ◽

Safety And Stability ◽

Network Public Opinion

The popularity of the Internet and the rise of self-media have built a diversified, convenient and instant platform for colleges and universities to do a good job in education, teaching, propaganda and ideology, but at the same time it also brings challenges and problems of college network security and campus stability. Most emergencies in colleges and universities are caused by trivial incidents and are largely unpredictable. If they are put online through online channels, they will attract the attention of the majority of netizens in a short period of time and attract online public attention. Once it is not handled in a timely manner, it will affect the normal education and teaching of colleges and universities and the safety and stability of campuses, and it is likely to form major public opinion on a larger scale, affecting the harmony and stability of the local society. This article adopts the characteristics of the network public opinion of colleges and universities in Gansu Province. It analyzes the current situation and focuses on the countermeasures to deal with network public opinion caused by emergencies in colleges and universities, hoping to provide a reference for further promoting the level and effectiveness of network public opinion in response to emergencies in Gansu Province.

Download Full-text

Research on Mining and Application of Group Events Based on Network Public Opinion Big Data

Journal of Web Engineering ◽

10.13052/jwe1540-9589.2069 ◽

2021 ◽

Author(s):

Weimin Gao ◽

Jiaming Zhong ◽

Yuan Xiao

Keyword(s):

Big Data ◽

Public Opinion ◽

Social Life ◽

The Internet ◽

Sir Epidemic Model ◽

Control Effect ◽

Public Events ◽

Sir Epidemic ◽

The Government ◽

Network Public Opinion

Network Public Opinion is significant in maintaining social harmony and stability and promoting transparency in government affairs. However, with the development of economy and transformation of society, our country has entered a high-risk period, which is full of unexpected public events. Unexpected mass accidents also cause hot discussions among the Internet users once they are exposed on the network. Different ideas, opinions, emotions, and attitudes about unexpected public events will be collected and collide on the Internet. It makes Network Public Opinion play an increasingly important role in the evolution of unexpected public events. It could promote the spread and upgrade of unexpected public events and bring more and more profound influence on to our social life. We use the case study method to analyze and solve the problems by applying the dynamic principles of the SIR epidemic model, comprehensively considering the social environment and various influencing factors, and constructing a mathematical model for the spread of network group events. The study uses Matlab to simulate the change trajectory of the number of participants in the network group events. By adjusting the number of contacts φ in the model, the development of network group emergencies can be effectively controlled and managed. As long as the government takes timely intervention measures, the dissemination of network group events can be basically controlled. Combined with public opinion big data to discover the important factors affecting the spread of public opinion, the control effect is obvious.

Download Full-text

Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

Electronics ◽

10.3390/electronics10232921 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2921

Author(s):

Xiaolin Li ◽

Zhiyi Li ◽

Yahe Tian

Keyword(s):

Public Opinion ◽

New Media ◽

Sentiment Analysis ◽

Topic Model ◽

Colleges And Universities ◽

Mobile Internet ◽

Knowledge Graph ◽

Analysis Model ◽

Governance System ◽

Network Public Opinion

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.

Download Full-text

Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

CONVERTER ◽

10.17762/converter.230 ◽

2021 ◽

pp. 559-565

Author(s):

Peng Bo, Xu Xiao-Long

Keyword(s):

Big Data ◽

Public Opinion ◽

Quantitative Research ◽

Prediction Models ◽

Control Model ◽

Government Control ◽

Mechanism Model ◽

The Government ◽

And Control ◽

Network Public Opinion

It is the key for the government to control the degree of information alienation to study the mechanism and control model of network public opinion information alienation for big data. This provides a theoretical basis for the government to deal with and manage the network public opinion. This paper uses qualitative analysis of the information alienation mechanism of network public opinion under the big data environment, and expands the evolution mechanism model of network public opinion to the information alienation control model. On this basis, the classification of government control information alienation is studied by numerical simulation. This paper takes the actual forum, blog, website with news comment function as the research object, and proposes a prediction platform construction scheme based on Java, which integrates a variety of prediction models. This provides useful exploration and ideas for quantitative research on the complex social phenomenon of network public opinion.

Download Full-text