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

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.

scholarly journals Towards a Bibliometric Mapping of Network Public Opinion Studies

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 17
Author(s):  
Yujie Qiang ◽  
Xuewen Tao ◽  
Xiaoqing Gou ◽  
Zhihui Lang ◽  
Hui Liu
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Knowledge Base ◽  
Public Management ◽  
Current Status ◽  
Future Research ◽  
Bibliometric Mapping ◽  
User Influence ◽  
Depth Analysis ◽  
Network Public Opinion

To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO.

scholarly journals Multimedia Network Public Opinion Supervision Prediction Algorithm Based on Big Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yangfan Tong ◽  
Wei Sun
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Prediction Model ◽  
Short Term Memory ◽  
Evaluation Model ◽  
Prediction Algorithm ◽  
Multimedia Network ◽  
The Difference ◽  
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.

scholarly journals Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chris Bauer ◽  
Ralf Herwig ◽  
Matthias Lienhard ◽  
Paul Prasse ◽  
Tobias Scheffer ◽  
...  
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Survival Data ◽  
Scientific Literature ◽  
Entity Recognition ◽  
Literature Mining ◽  
Cancer Drugs ◽  
Classical Text ◽  
Anti Cancer ◽  
Cancer Types

Abstract Background There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap. Conclusions Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

Deep Structured Learning for Natural Language Processing

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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