Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data
This article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6623108 |
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| _version_ | 1832554555435384832 |
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| author | Yangfan Tong Wei Sun |
| author_facet | Yangfan Tong Wei Sun |
| author_sort | Yangfan Tong |
| collection | DOAJ |
| description | This article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion, deeply summarizes the network public opinion in the era of big data. New features analyze the opportunities and challenges faced by online public opinion in the era of big data. Based on the rational construction of the index system, this paper studies the multimedia network public opinion evaluation and prediction algorithm. Existing network public opinion assessment and prediction algorithms have shortcomings in capturing the characteristics of data sequences and the long-term dependence of data sequences, and the problems of overfitting and gradient disappearance may occur during training. Because of the above problems, based on the long-term and short-term memory network model, a regularized method is used to construct a multimedia network public opinion prediction model algorithm. This paper builds a multimedia network public opinion threat rating evaluation model based on the public opinion supervision prediction model and conducts analysis. The model constructed this time can not only improve the accuracy of public opinion assessment and prediction but also better avoid the problem of gradient disappearance and overfitting. |
| format | Article |
| id | doaj-art-31d63dfdb00a4151a2843393d3e44f10 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-31d63dfdb00a4151a2843393d3e44f102025-02-03T05:51:11ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66231086623108Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big DataYangfan Tong0Wei Sun1School of Art, Wuhan University, Wuhan 430072, ChinaSchool of Literature and Journalism, Hunan University of Technology and Business, Changsha 410205, ChinaThis article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion, deeply summarizes the network public opinion in the era of big data. New features analyze the opportunities and challenges faced by online public opinion in the era of big data. Based on the rational construction of the index system, this paper studies the multimedia network public opinion evaluation and prediction algorithm. Existing network public opinion assessment and prediction algorithms have shortcomings in capturing the characteristics of data sequences and the long-term dependence of data sequences, and the problems of overfitting and gradient disappearance may occur during training. Because of the above problems, based on the long-term and short-term memory network model, a regularized method is used to construct a multimedia network public opinion prediction model algorithm. This paper builds a multimedia network public opinion threat rating evaluation model based on the public opinion supervision prediction model and conducts analysis. The model constructed this time can not only improve the accuracy of public opinion assessment and prediction but also better avoid the problem of gradient disappearance and overfitting.http://dx.doi.org/10.1155/2020/6623108 |
| spellingShingle | Yangfan Tong Wei Sun Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data Complexity |
| title | Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data |
| title_full | Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data |
| title_fullStr | Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data |
| title_full_unstemmed | Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data |
| title_short | Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data |
| title_sort | multimedia network public opinion supervision prediction algorithm based on big data |
| url | http://dx.doi.org/10.1155/2020/6623108 |
| work_keys_str_mv | AT yangfantong multimedianetworkpublicopinionsupervisionpredictionalgorithmbasedonbigdata AT weisun multimedianetworkpublicopinionsupervisionpredictionalgorithmbasedonbigdata |