scholarly journals Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090421 ◽  
Author(s):  
Fengzhen Jia ◽  
Chun-Chun Chen
Keyword(s):  
Time Series ◽  
Public Opinion ◽  
Rapid Development ◽  
Practical Application ◽  
Analysis Model ◽  
Evolution Analysis ◽  
Proposed Model ◽  
Tendency Analysis ◽  
Emotional Lexicon ◽  
Network Public Opinion

In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.

scholarly journals Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ying Zhu
Keyword(s):  
Time Series ◽  
Public Opinion ◽  
Chaotic Time Series ◽  
Edge Computing ◽  
Factor Graph ◽  
New Paradigm ◽  
Placement Problem ◽  
Index Method ◽  
Service Placement ◽  
Network Public Opinion

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

scholarly journals Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2921
Author(s):  
Xiaolin Li ◽  
Zhiyi Li ◽  
Yahe Tian
Keyword(s):  
Public Opinion ◽  
New Media ◽  
Sentiment Analysis ◽  
Topic Model ◽  
Colleges And Universities ◽  
Mobile Internet ◽  
Knowledge Graph ◽  
Analysis Model ◽  
Governance System ◽  
Network Public Opinion

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.

scholarly journals Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions

2020 ◽  
Vol 17 (18) ◽  
pp. 6681
Author(s):  
Peihua Fu ◽  
Bailu Jing ◽  
Tinggui Chen ◽  
Jianjun Yang ◽  
Guodong Cong
Keyword(s):  
Public Opinion ◽  
Attitude Change ◽  
Rapid Development ◽  
Small World ◽  
Inhibitory Effect ◽  
Propagation Model ◽  
Clustering Coefficient ◽  
Factors Affecting ◽  
Scale Free ◽  
Network Public Opinion

The occurrence of popular social events causes fluctuations and changes of public emotions, while the rapid development of online social platforms and networks has made individual interactions more intense and further escalated public emotions into public opinion. However, there is a lack of consideration of individual emotions in the current research on online public opinion. Based on this, this paper firstly expounds the quantitative representation of attitude and emotion, analyzes the formation and propagation process of online public opinion by combining individual’s expression willingness, individual’s expression ability, attitude perception value, attitude change probability and other factors, and constructs a network public opinion propagation model that takes individual emotion into consideration. Finally, the main factors affecting the formation and propagation of network public opinion are discussed through simulation experiments. The results demonstrate that: (1) fear is conducive to the formation of online public opinion, but the speed is relatively slow; sadness is not conducive to the formation, but once enough people participate in the exchange of views, the formation of online public opinion will be faster; (2) the influence of online public opinion on individual emotions expands with the increase of the number of individual interactions; (3) different network structures impact differently on the propagation of public opinion. Among them, BA (BA network is a scale-free network model proposed by Barabasi and Albert in order to explain the generation mechanism of power law, BA model has two characteristics: growth and priority connection mechanism) and ER (ER network is a network with random connectivity proposed by Erdös-Renyi) random networks can promote the propagation of online public opinion, which is prone to “one-sided” online public opinion. WS small-world networks (proposed by Watts and Strogatz. It is a kind of network with short average path length and high clustering coefficient) and fully-connected networks have an inhibitory effect on the spread of online public opinion, easily maintaining the multi-dimensional nature of online public opinion.

scholarly journals The Influence of Network Public Opinion on the Public Management of Chinese Government

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Yichu Wang
Keyword(s):  
Public Opinion ◽  
Public Management ◽  
Daily Life ◽  
Rapid Development ◽  
Science And Technology ◽  
The Internet ◽  
Chinese Government ◽  
The Public ◽  
Network Public Opinion

In the rapid development of science and technology, the Internet has been widely used in the daily life and work of people, which has greatly changed the way people live and work. At this stage, people regard the Internet as the main way to obtain news information, and they have supervised the news contents [1]. Based on this, the article expounds the relevant content of network public opinion, analyzes the role of network public opinion in the public management of Chinese government, and studies the influence of public opinion on the public management of Chinese government.

scholarly journals Network public opinion propagation model based on the influence of media and interpersonal communication

2019 ◽  
Vol 33 (32) ◽  
pp. 1950393 ◽  
Author(s):  
Yue-Xia Zhang ◽  
Yi-Xuan Feng ◽  
Rui-Qi Yang
Keyword(s):  
Public Opinion ◽  
Interpersonal Relationships ◽  
Media Coverage ◽  
Rapid Development ◽  
Propagation Model ◽  
Practical Significance ◽  
Seir Model ◽  
Model Based ◽  
The Impact ◽  
Network Public Opinion

With the rapid development of the Internet, social media networks have become the primary platform for people to express their views. In addition, network public opinion has a considerable influence on society. Thus, considering the significant impact of online public opinion on society, it is necessary to study and analyze the propagation process for public opinion. In this study, we propose the Media and Interpersonal Relationship-SEIR (MI-SEIR) model based on the SEIR model. Our proposed model considers the impact of media transmission and interpersonal relationships on opinion propagation. Our MI-SEIR model divides the propagation nodes into three categories: support, neutral and opposition. There is a discussion mechanism between these nodes that represents the node’s viewpoint value evolution rule based on the node firmness, influence of nodes, quality of media coverage and parameters of infection. The state transition of nodes is decided based on the change of opinion value. Our simulation experimental results show that this model is more representative of the real propagation of online public opinion and is thus of practical significance for research and opinion analysis applications.

scholarly journals Application of Artificial Intelligence in Modern Art Teaching

2020 ◽  
Vol 15 (13) ◽  
pp. 238 ◽  
Author(s):  
Fanwen Kong
Keyword(s):  
Artificial Intelligence ◽  
Rapid Development ◽  
Modern Art ◽  
Design Strategies ◽  
Analysis Model ◽  
Current Application ◽  
Proposed Model ◽  
Art Teaching ◽  
Grey Clustering ◽  
Application Potential

Despite its rapid development, the artificial intelligence (AI) has not been deeply applied in art teaching. Hence, this paper attempts to design strategies for applying AI in art teaching. For this purpose, the authors reviewed the current application status of AI in art teaching and summed up the problems with these applications. Next, the promotive role of AI in art teaching was analyzed in detail, and three strategies were developed to promote AI applications in modern art teaching, namely: expand the adaptability of AI-based art teaching; improve the intelligent teaching mode of art teaching; and enhance the artistic experience and atmosphere of AI-based art teaching. At the same time, to evaluate the application effect of AI in art teaching, a performance analysis model was designed based on analytical hierarchy process (AHP) and grey clustering. The proposed model can quantify the application effect of AI in art teaching, and it has a good application potential.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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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