Abstract
The multi-dimensional characteristics of public opinion in online education lead to the difficulty of data cross-dimensional mining. To solve this problem, this paper designs a cross-dimensional data mining model of public opinion in online education based on fuzzy association rules. Based on the public opinion subject, object, and ontology to analyze the characteristics of public opinion in online education, Yaahp software is used to calculate the influence factor weight of public opinion in online education. According to the weight analysis results, the relationship between the dimensions of various public opinion data is clarified by using data semantic association. This paper introduces the fuzzy set theory into the database and uses crawlers to obtain public opinion data and stores them in the database, to complete the data preprocessing through distributed text preprocessing, feature selection distributed computing, and text vectorization distributed computing. Taking the cloud computing platform as the core, the cross-dimension mining model of public opinion in online education data is constructed according to the dimension correlation analysis and preprocessing results. The simulation results show that the model has the advantages of wide range, fast speed, and high accuracy, and can provide data support for education reform.
Highlights
As the rapid growth of information techniques utilized in different domains and applications [1,2,3,4,5,6], public opinion in online education refers to the tendentious individual attitude and subjective will expressed by netizens around the occurrence, development, and change of a certain educational phenomenon in the virtual space of the Internet
Based on fuzzy association rules, taking the cloud computing platform as the core, this paper constructs a cross-dimension mining model of public opinion data in online education according to the results of dimension association analysis and preprocessing, to lay a solid foundation for public opinion analysis in online education
To verify the validity of the cross-dimension mining model of public opinion data in online education based on fuzzy association rules, an experimental test is conducted
Summary
As the rapid growth of information techniques utilized in different domains and applications [1,2,3,4,5,6], public opinion in online education refers to the tendentious individual attitude and subjective will expressed by netizens around the occurrence, development, and change of a certain educational phenomenon (including ideas, events, figures, policies, problems, etc.) in the virtual space of the Internet. Zhang et al [15] proposed a quantitative data mining method based on improved multi-level fuzzy association rules. The method uses high-frequency item sets, forming as a top-down mining process by deepening iteration, integrating the fuzzy-set theory, data mining algorithm, and multi-level classification technologies to find fuzzy association rules from transaction data sets. Wang et al [16] proposed a comprehensive association rule mining method for health examination data based on the extended FPGrowth method This method extends the FP-Growth algorithm for mining positive and negative frequent patterns, namely the PNFP-Growth framework. With the support of the cloud computing platform, this paper uses fuzzy association rules to build an accurate and effective data mining model, to provide help for the reform and development of education in China
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
