Network Public Opinion Analysis Research Articles

Campus Network Public Opinion Monitoring System Based on Reptile Technology

This paper conducts a lot of research on campus network public opinion analysis, and uses the hybrid crawling strategy commonly used in academic circles to focus on the key technologies of network public opinion analysis such as information preprocessing, Chinese word segmentation and topic recognition. Finally, the analytic hierarchy process and wavelet neural network are used to design the public opinion analysis system, and all the modules in the system are organically combined to develop an efficient and feasible network public opinion monitoring system, and the Sade event is taken as an example to express the public opinion. Analysis and trend prediction, the experimental results show that the system has good evaluation performance and estimation accuracy.

Research on Network Public opinion Analysis Platform Architecture Based on Big Data

With the development of Internet technologies and applications, network public opinion has undergone tremendous changes in terms of data volume, complexity, and speed of production. Existing public opinion analysis techniques and monitoring systems are difficult to adapt to changes in this scenario, and must be innovative under the thinking of big data analysis. Therefore, this paper designs a network public opinion analysis platform through the use of big data technology, including information collection, information processing, information analysis, public opinion display and information security. And from the three aspects of government decision-making, hotspot tracking, and public opinion guidance, the application effect of the platform is proposed, and the innovation of thinking, mode and technology of network public opinion management is realized. It has important theoretical and practical value for network public opinion management.

Online Public Opinion Guidance Strategy for College Students in the Era of We Media

In the era of We Media, online public opinion in colleges and universities has become an important front of ideological and political, with some characteristics such as concealment, diversity and sensitivity, interaction and immediacy of communication. It is urgent to carry out some researches about network public opinion analysis and guidance mechanism. It is a new challenge that the ideological and political education in colleges and universities, must face how to guide online public opinion effectively. The team building should be strengthened and the right of public opinion should be returned. The platform construction should be enriched, and it is important to give the chance to various media. Therefore, it should strengthen the mechanism construction, and public opinion guidance must be scientific and professional. Colleges and universities should build a clear network space from such aspects as the construction of network public opinion guidance institutions, the cultivation of opinion leaders, the perfection of early warning mechanism, the improvement of university network literacy and the guidance of campus media.

Latent semantic text classification method research based on support vector machine

Text classification, as an important process of network public opinion analysis, will directly affect the judgment of text public opinion. The accuracy of text classification is an important prerequisite for textual public opinion analysis. At present, the commonly used text classification methods mainly focus on clustering and machine learning. In general, the accuracy is not ideal. Moreover, text classification method based on latent semantics has the characteristics of insensitivity to feature dimension and simple classification methods, so it has become the focus of extensive research. However, as the type of text increases, local semantic analysis will occur, resulting in the dropping of classification accuracy of text. In this paper, a latent semantic classification method based on support vector machine (LR-LSA) is proposed to solve the problem of local semantic analysis brought by too much text category, it can be better to solve the impact of feature dimension surge on effect.

Latent semantic text classification method research based on support vector machine

Text classification, as an important process of network public opinion analysis, will directly affect the judgment of text public opinion. The accuracy of text classification is an important prerequisite for textual public opinion analysis. At present, the commonly used text classification methods mainly focus on clustering and machine learning. In general, the accuracy is not ideal. Moreover, text classification method based on latent semantics has the characteristics of insensitivity to feature dimension and simple classification methods, so it has become the focus of extensive research. However, as the type of text increases, local semantic analysis will occur, resulting in the dropping of classification accuracy of text. In this paper, a latent semantic classification method based on support vector machine (LR-LSA) is proposed to solve the problem of local semantic analysis brought by too much text category, it can be better to solve the impact of feature dimension surge on effect.

A Method of Network Public Opinion Analysis Based on Quantum Particle Swarm Algorithm Optimization Least Square Vector Machine

Prediction of network public opinion is a complicated prediction featuring poor information, small samples and uncertainty. A prediction model of network public opinion based on grey support vector machine (SVM) is specified to increase prediction accuracy. First, network data are preprocessed by text clustering, hotpot extraction and data aggregation. Then a time series model GM(1,1) is established and SVM is used to modify prediction outcomes of GM(1,1). At last, simulation experiment is conducted to test performance of the model. Simulation results indicate that grey SVM improves the prediction accuracy of network public opinion compared with traditional prediction models. The predictions have certain practical values.

A Survey of Key Technology of Network Public Opinion Analysis

The internet has become an important base for internet users to make comments because of its interactivity and fast dissemination. The outbreak of internet public opinion has become a major risk for network information security. Domestic and foreign researchers had carried out extensive and in-depth study on public opinion. Fruitful results have achieved in the basic theory research and emergency handling and other aspects of public opinion. But research on the public opinion in China is still in the initial stage, the key technology of the public opinion analysis is still as a starting point for in-depth study and discussion.

Design and Implementation of Network Public Opinion Analysis System

Network public opinion analysis is an important way of information analysis processing. This paper based on the research of the related technologies, designs and realizes a new network public opinion analysis system. System mainly includes network data fetching part, fetching the data processing part, analyzes the processed data part and display part of the public opinion analysis results. In the document extraction part, used the web crawler technology, Larbin web crawler to realize the collection of web content; In public opinion information analysis part, the implementation of the new topic adopts an improved Single - Pass clustering algorithm. This algorithm is using of multi-center, using the title and body of the vector to compared two-way ,that is better reflect the dynamics of public opinion topics. Finally, in the network environment of a university, we have the tests repeatedly. The results show that the new public opinion analysis system running is stable and has good efficiency. The thesis has certain value for the development of other information analysis systems in the Internet.

Research and application of public opinion retrieval based on user behavior modeling

This paper designs a system of network public opinion analysis based on the specific way of “matching Topic with Opinion” which can organize public opinion data, avoid the redundant data and retain the original information structure of the opinion. And this article proposes a user model founded on the user access behavior to scientifically classify and represent the relevant theory of the users’ retrieval behavior, then to analyze and manage the retrieval results which can to provide the accurate and relevant search results of public opinion information for users.

The Research of Network Public Opinion Hotspots Technologies for Internet Web

Network public opinion analysis, which is through the analysis of the situation of public opinion, can reflect the true society and public opinion, and on this basis, a reasonable forecast and recommendations decision-making can be provided for relevant managers. The paper studies on agricultural science and technology related website, and realize internet public opinion analysis for agriculture science and technology site. The network public opinion hotspots found technology is studied. It is achieved internet public opinion hotspots monitoring and found through the network information automatically grab and information classification. It is provided an analytical basis for managers to grasp the ideological trends in the network and make the correct guidance.