Research on Classification of Multimedia Network Public Opinion

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.

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Interoperability of Multimedia Network Public Opinion Knowledge Base Group Based on Multisource Text Mining

Advances in Multimedia ◽

10.1155/2021/9339584 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yanru Zhu

Keyword(s):

Public Opinion ◽

Text Mining ◽

Knowledge Base ◽

Research Literature ◽

Communication Process ◽

Strong Basis ◽

Social Events ◽

Multimedia Network ◽

Base Group ◽

Network Public Opinion

Internet media has gradually replaced the existence of traditional media and has become the main place for people to express their views and opinions on social events. Based on the huge user base, after social events occur, a large number of Internet users promote the derivation and dissemination of topics to form Internet public opinion. The fast communication process and wide communication coverage have brought higher requirements to the supervision of Internet public opinion. Internet public opinion is an important expression of sociological intelligence at present, and multisource text mining technology has become a commonly used form of expression based on unstructured data by research scholars, providing relatively important technical support for public opinion information data analysis. After analyzing the relevant research literature of the multimedia network knowledge base groups in detail, this paper analyzes the operating factors and mechanisms among the multimedia network knowledge base groups elaborately. Finally, it is applied to the process of network public opinion analysis. The results of the case analysis show that the multisource text mining algorithm can provide a strong basis for the construction of a multimedia network public opinion knowledge base group.

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A New Kind of Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Platform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2018 ◽

2014 ◽

Vol 644-650 ◽

pp. 2018-2021 ◽

Cited By ~ 2

Author(s):

Bin Ma

Keyword(s):

Public Opinion ◽

Nearest Neighbor ◽

Classification Algorithm ◽

Classification Method ◽

Hadoop Platform ◽

Classification Test ◽

Network Public Opinion

According to the network public opinion’s characteristics of distributed, massive and heterogeneous, a new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop plateform is studied. The classification ability and execution efficiency of proposed scheme is verified and applied to the network public opinion documents classification test. The results show that the parallel K_NN algorithm can achieve rapid and accurate classification of network public opinion.

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Deep Structured Learning for Natural Language Processing

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3433538 ◽

2021 ◽

Vol 20 (3) ◽

pp. 1-14

Author(s):

Yong Li ◽

Xiaojun Yang ◽

Min Zuo ◽

Qingyu Jin ◽

Haisheng Li ◽

...

Keyword(s):

Public Opinion ◽

Food Safety ◽

Language Processing ◽

Early Warning ◽

Conditional Random Field ◽

Semantic Features ◽

Related Sequence ◽

The Public ◽

Network Public Opinion

The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

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