Public Opinion Events Research Articles

Emotion recognition of social media users based on deep learning.

Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, "joy," "anger," and "sadness," are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) model, emotion dictionary, and manual annotation is created to extract emotional feature words. The captured motivational text is converted into a word vector using Word2vec. After gathering the long-distance semantic data with bidirectional long short-term memories (BiLSTM) and convolutional neural networks (CNN) extract the text's key characteristics to finish the emotion categorization. The test results demonstrate an average increase in F1 value of 3.66 percent for six machine learning models and an average increase in F1 value of 1.84 percent for seven deep learning models. The suggested model performs better at identifying the emotions of social media users than the current machine learning and deep learning methods.

Public Management Model Based on Mathematical Equation of Statistics of Probability Theory

Abstract Firstly, this paper establishes a cloud computing public opinion management business model based on distributed collection analysis, consultative decision and concurrent collaborative behavior. At the same time, this paper studies the public opinion management system from the logical framework, network structure and security index. Then this paper uses the weak correlation mining technology of probability theory statistics to establish a public health and safety public opinion prediction system composed of three dimensions. The content includes three levels of public opinion bonding, thermal and trend. Then, this paper forecasts the development of public opinion events by combining K-Means clustering and grey correlation degree. The results show that this method is suitable for public opinion prediction in public health and safety.

Positive public opinion guidance model based on dual learning in social network

Internet public opinion is closely related to our life in social network. The wanton growth of some negative public opinions has an extremely serious impact on the social stability and national security. After the guidance of government manually, some negative public opinion is well controlled and people’s life gain more positive energy. How to use Internet technology to automatically and promptly guide public opinion events and reduce the harm to society is currently challenging research. Therefore, in this paper, we propose a positive public opinion guidance model based on dual learning for negative Internet public opinion, hereinafter denoted to as the dual-PPOG model. Firstly, we use the Fast Unfolding algorithm to divide social networks into the public opinion guidance communities. In these communities, we detect the positive opinion guider and negative opinion receiver by our defined PageRank (PR) variant. Secondly, inspired by dual learning, we construct the public opinion guidance model and evaluate whether the guidance is successful through the feedback signal. Through the repeated guidance of the positive opinion guider to the negative opinion receiver, the public opinion guidance is successful. This is the main process for the dual positive public opinion mechanism. Finally, we guide the remaining nodes based on the opinion dynamics. The experiment demonstrates beneficial effects of our proposed model of dual-PPOG. Experimental results on three real-world datasets intercepted from Twitter, E-mail and Facebook show that the model of dual-PPOG can capture useful information in the network topology. Compared with the methods of HK, AE, Random and AIA on the three datasets from small to large in scale, the percentage of positive opinion increased by 4%, 6.9%, and 2.7% respectively, which shows our approach achieve significant improvements and effectiveness compared to all baselines.

Influences of Populism on Youth in Social Media

In recent years, the global spread of COVID-19 has brought significant instability and uncertainty to the lives of people worldwide while also providing an opportunity to spread populist ideas. Especially in recent years, with the development of Internet technology, populism and the internet have fused to form a "cyber-populist" social trend. This online populism has a wide range of influence and destructive solid power, bringing a great crisis to global ideological security. At the same time, online populism is also insidiously attacking young people. It is mainly manifested in the birth of a destructive mentality among young people, inducing them to blindly follow the crowd, lose their independent personalities, and polarise their value judgments. This paper analyses typical public opinion events and their data in recent years by searching the internet and the Civiw website; in the light of the characteristics of youth expression in the Internet era, the negative impact of online populism on the psychological development of youth is explored. The paper goes on to discuss the reasons for the negative impact of online populism on the psyche of young people: inadequate mechanisms for monitoring online opinion at universities, insufficient protection of young people's awareness of the rule of law online, and a lack of online activism among young people. Only by promoting social justice from a macro perspective, controlling the gathering of online groups, using laws to channel public psychology and bringing online speech back to mainstream ideology is the only way to move the youth population from impulsiveness to rationality, and it is also the way to address the dangers posed by online populism.

Applying the DEMATEL−ANP Fuzzy Comprehensive Model to Evaluate Public Opinion Events

Network public opinion is a mirror reflecting people’s will, and evaluating its urgency can help to find hidden social crises. Research on public opinion in the field of machine learning usually focuses on micro-sentiment judgment, which is unable to offer support for the evaluation of public opinion events without additional data, and research from the perspective of artificial weighting has the disadvantage of the confusion of explanation. Judging the urgency of public opinion events is usually based on human perception, which is fuzzy and conforms to the attribute of fuzzy mathematics. Therefore, the index system in this paper was constructed in line with five principles, from which the weights were scientifically evaluated by integrating the DEMATEL and ANP model, and fuzzy mathematics was applied to determine the urgency level of public opinion. The result has three-fold significance. First, the index system constructed was more closely linked. Second, the integration of the DEMATEL and ANP weight calculating model took the interdependence of indicators fully into account. Third, fuzzy mathematics provided support for determining the public opinion crisis level, especially in the absence of immediate dissemination data.

Research on Lovelorn Emotion Recognition Based on Ernie Tiny

Topics related to sentiment classification and emotion recognition are an important part of the Natural Language Processing research field and can be used to analyze users' sentiment tendencies towards brands, understand the public's attitudes and opinions on public opinion events, and detect users' mental health, among others. Past research has usually been based on positive and negative emotions or multi-categorized emotions such as happiness, anger and sadness, while there has been little research on the recognition of the specific emotion of lovelorn. This study aims to identify the lovelorn emotion in text, using deep learning pretrained model ERNIR Tiny to train a dataset consisting of 5008 pieces of Chinese lovelorn emotion text crawled from social media platform Weibo and 4998 pieces of ordinary text extracted from existing available dataset. And finally, it was proved that ERNIE Tiny performs well in classifying whether a text contains lovelorn emotion or not, with F1 score of 0.941929, precision score of 0.942300 and recall score of 0.941928 obtained on the test set.

Event Feature Pre-training Model Based on Public Opinion Evolution

The comments in the evolution of network public opinion events not only reflect the attitude of netizens towards the event itself, but also are the key basis for mastering the dynamics of public opinion. According to the comment data in the event evolution process, an event feature vector pre-training model NL2ER-Transformer is constructed to realize the real-time automatic extraction of event features. Firstly, a semi-supervised multi-label curriculum learning model is proposed to generate comment words, event word vectors, event words, and event sentences, so that a public opinion event is mapped into a sequence similar to vectorized natural language. Secondly, based on the Transformer structure, a training method is proposed to simulate the evolution process of events, so that the event vector generation model can learn the evolution law and the characteristics of reversal events. Finally, the event vectors generated by the presented NL2ER-Transformer model are compared with the event vectors generated by the current mainstream models such as XLNet and RoBerta. This paper tests the pre-trained model NL2ER-Transformer and three pre-trained benchmark models on four downstream classification models. The experimental results show that using the vectors generated by NL2ER-Transformer to train downstream models compared to using the vectors generated by other pre-trained benchmark models to train downstream models, the accuracy, recall, and F1 values are 16.66%, 44.44%, and 19% higher than the best downstream model. At the same time, in the evolutionary capability analysis test, only four events show partial errors. In terms of performance of semi-supervised model, the proposed semi-supervised multi-label curriculum learning model outperforms mainstream models in four indicators by 6%, 23%, 8%, and 15%, respectively.

安全视域下公共舆论事件中的网络语言暴力研究

安全视域下公共舆论事件中的网络语言暴力研究

Research on College Network Early Warning Based on LSTM

With the development of information technology and the continuous optimization of China’s education system, college students have gradually become the main group in the Internet, and college network public opinion events have received increasing attention. By analyzing the constituent elements of university network public opinion, building an early warning indicator system from three dimensions: subject, carrier, and object, and combining life cycle theory, dynamic comprehensive evaluation method, and short-term memory network, constructing a university network public opinion model. Through empirical analysis of 20 university network public opinion events, the experimental results show that the proposed public opinion model has certain feasibility. Corresponding response strategies have also been proposed for different levels of crisis warning, providing a decision-making basis for relevant departments to guide and manage online public opinion in universities.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend.

Combining NSP and NER for public opinion event extraction model

Event extraction in the field of public opinion aims to extract important event arguments and their corresponding roles from the moment-to-moment generated opinion reports. Most of the existing research methods divide the task into three subtasks: event trigger extraction, event type detection, and event argument extraction. Despite the remarkable achievements of the event argument extraction paradigm combining part-of-speech (POS) and event trigger features, the performance of POS features in combinatorial event argument extraction tasks is struggling due to its inherent semantic diversity in Chinese. In addition, previous research work ignored the deep semantic interaction between event trigger and text. To address the aforementioned problems, this paper proposes an opinion event extraction model (NN-EE) combining NSP and NER, which alleviates the lack of performance of combinatorial event argument extraction by introducing NER technology. Meanwhile, the event trigger features are incorporated into the NSP mechanism of the pre-trained language model BERT to prompt the model to learn the deep semantic interaction between the event trigger and original text. The results of the self-constructed food opinion report dataset (FD-OR) in this paper show that the NN-EE model achieves optimal performance.

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

With the continuous expansion of colleges and universities, the proportion of college students among netizens has increased rapidly. They have become an important component of Chinese netizens and the main audience of Weibo. It is easier for online public opinion to form in some social media platforms than in others. We explain places for political conversation in Chinese cyberspace in terms of interaction, which results in various types of political discussion, based on research on authoritarian deliberation. The crisis of college network public opinion caused by the improper use of Weibo by college students occurs frequently. College network public opinion has become an important factor affecting the development and stability of colleges and universities. Social network rumor forwarding behavior refers to whether users forward specific rumors. Also, there is no surety about the truthfulness of the rumor. Taking Weibo as an example, this paper proposes a neural network-based model for predicting the forwarding volume of public opinion emergencies on campus Weibo. It solves the problem of low prediction accuracy of traditional SVM and other models. The data-driven experimental findings suggest that the technique described in this study can increase the accuracy of predicting forwarding volume in unexpected campus public opinion events.

Analysis of Sports Popular Trend Based on Public Opinion Mining of New Media

As an important means of communication, sports public opinion has been paid more and more attention by the country. This article will focus on the emergency events of sports events as the main object and research and analysis of the communication function and law in the public opinion of sports events and analyzes the characteristics of public opinion communication. This paper holds that the media play the “should be” roles in different periods, such as network public opinion monitoring and early warning, public opinion information processing and interpretation, consensus builder highlighting the mainstream, and introspection of public opinion events, and there is a media role cycle in the secondary public opinion period. Spreading law of public opinion in sports events in China: trigger-conduction-outbreak mechanism. At present, the diversified interactive mode of sports events in China makes the communication environment of public opinion in sports events more and more three-dimensional and multidimensional, and it is also convenient for us to make better use of the current regulation means of public opinion in sports events. Sports network public opinion can easily go to “post-truth.” In order to strengthen the monitoring of sports public opinion and improve the ability to respond to network public opinion, using interdisciplinary thinking, this paper proposes a network public opinion guidance strategy for sports events. The experiment is aimed at the disadvantage that the trend of sports public opinion analysis and tracking is not clear. The optimization method is proposed, and the accuracy is improved by 87.5%.

Online public opinion prediction based on a novel seasonal grey decomposition and ensemble model

Due to the influence of netizens’ behaviors, social activities, media and other factors, the trend of online public opinion shows the characteristics of nonlinear and seasonal fluctuation, but most researchers ignored it. In order to accurately predict the hot-degree of online public opinion, this paper proposes an improved seasonal grey decomposition and ensemble model. The STL decomposition algorithm is used to decompose original public opinion data. And the grey modified exponential model is proposed based on the grey difference information. Then the dynamic seasonal factors and Bernoulli equation are introduced to establish the seasonal modified exponential grey Bernoulli model. The SMEGBM model is used to predict the seasonal sequence and trend sequence, and the ARIMA model is used to predict the remainder sequence. In order to validate the prediction effect of the new model, the hot-degree predication of “Lin Shengbin” and “Tangshan beating” online events are implemented for empirical analysis. Compared with other models, the model proposed in this paper shows higher prediction accuracy. The results show that it is necessary to take the periodicity into account in the establishment of network public opinion model. And the hybrid model can provide theoretical supports for relevant departments to monitor and give early warning of sudden online public opinion events.

Modeling and Simulation Research of Interactive Public Opinion Evolution under Multi-Agent Interventions

To study the interactive impact and multi-agent intervention effect caused by multiple public opinion events, a SI3R interactive public opinion communication model is proposed in this paper. Based on interactive public opinion evolution and influence paths of multi-agent interventions, the dynamic equation under multi-agent intervention is formulated; the equilibrium points of the model are calculated and the stability of the model is validated; and the assignment functions are constructed. Further, real data of the case study are obtained and set as initial model simulation parameters, the multi-agent intervention and control measures are numerically simulated and the effects of comprehensive public opinion control are obtained and put forward to suggestions for public opinion management. Simulation results show that the model can simulate the interactive propagation and evolution trend of multiple public opinion events, and imitate the intervention effect of each agent. Compared with the traditional transmission and infection model, it provides an assignment method combined with actual events. Taking the interaction of multiple public opinion events and the effect of multi-agent intervention into account, a new perspective for the formulation of public opinion supervision and control strategies can be provided.

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

Corpus-Based Data Acquisition and Topic Analysis of Chinese-Related Public Opinion in Western Media

This paper sorts out and discusses the different methods used in the analysis of China-related public opinion, builds a theoretical system of public opinion analysis methods on this basis, conducts case analysis and research on hot public opinion combined with corpus, and selects the semantic analysis method from the technical level supporting the public opinion analysis method. From the perspective of related technologies involving intelligent analysis methods, comparative analysis and improved applications are given, which provides an effective analysis basis for government public management departments to fully grasp and guide China-related public opinion. Sentiment tendency analysis is also an important direction of Chinese-related public opinion research. This paper sorts and analyzes China-related public opinion from the characteristics, applications, and text processing methods of different granularities and builds and improves the annotation model for the smallest granularity. The frequency, trend, and evolution characteristics of China-related public opinion events are sorted out and analyzed, and the trend analysis method is used to analyze the evolution trend of attention of public opinion events. The distribution of individual opinion acceptance, trust threshold, and opinion leaders are simulated by experiments. The impact on the evolution of China-related public opinion was determined. The experimental results show that the effect of the method proposed in this paper is improved by about 10% compared with the direct use of statistical learning methods for emotional orientation analysis. The SVM method also has obvious advantages over the Bayesian method, especially the combination of the SVM method and the bigram method which gives the best results.

The Transfer Model and Guidance Strategy of Netizens' Emotions.

In the context of the COVID-19 pandemic, a large amount of information is gathered on Internet platforms, through which people express their opinions and vent their emotions. Emotional guidance of netizens has become an important part of social governance during turbulences caused by a so-called “infodemic”. This study focuses on the evolution and interaction of netizens' emotions after the occurrence of network public opinion events. First, the transfer model of netizens' emotions is constructed, and the significance of each parameter in the model is studied through simulation. Then, based on the model, we put forward the optimization method and quantitative method of guidance strategy of netizens' emotions. Finally, the empirical study proves the effectiveness of the model, which can provide a theoretical basis for the emotional guidance strategies after the outbreak of network public opinion events.

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.