Network Public Opinion Analysis Research Articles

Application Status of Hadoop in Data Cloud Computing

This paper summarizes the Hadoop platform and its application in network public opinion analysis, and emphasizes the advantages of Hadoop in big data storage and processing, as well as the development of cloud computing technology and its importance in data collection, processing and analysis. This paper also discusses the core technologies based on Hadoop and Spark, including keyword retrieval, web crawler and text mining, which together constitute a comprehensive toolkit for analyzing and managing online public opinion.

Social Network Public Opinion Analysis Using BERT-BMA in Big Data Environment

The existing social network public opinion analysis methods have problems such as poor semantic expression quality and weak detection ability in short texts. Therefore, a social network public opinion analysis method based on BERT-BMA is proposed. To normalize the comment text, the rumor text is initially transferred to a word vector matrix using the BERT (Bidirectional Encoder Representations from Transformer) model. The BiLSTM-based network architecture is subsequently employed to acquire the trace features of data transmission. Ultimately, this study employs the multi-head attention mechanism to extract feature information that is more significant in the analysis of online public opinion by mining the dependency relationships between users, resulting in increasing ability to detect public opinion emergencies. The experimental outcomes indicate that the results on the Twitter data set and Weibo dataset are superior to other comparative models.

Application of Deep Learning in Social Network Public Opinion Sentiment

With the rapid development of social network platform, in order to purify the network environment, prevent the abuse of public opinion information, and control public opinion in a very short time, this paper proposes the application research of deep learning in social network public opinion sentiment recognition and analysis. Through the construction of social network model and social network knowledge map and the analysis of key technology, a network public opinion algorithm based on deep learning is proposed, and a competitive public opinion information communication model in online social networks is constructed, and then a simulation experiment is conducted on the improved model of the constructed social networks. The result shows that in public opinion management and control, for users with larger node degree, this technology can effectively understand and obtain public opinion information in a very short time with faster and wider information propagation speed, so as to realize the effective control of public opinion information and the processing of massive information.

A fog‐computing architecture for network public opinion monitoring based on intelligent semantic recognition

AbstractCurrently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.

A Case Study of Chinese Sentiment Analysis of Social Media Reviews Based on LSTM

Network public opinion analysis is obtained through a combination of natural language processing (NLP) and public opinion supervision, and is crucial for monitoring public mood and trends. Therefore, network public opinion analysis can identify and solve potential and budding social problems. This study aims to realize an analysis of Chinese sentiment in social media reviews using a long short-term memory network (LSTM) model. A dataset was obtained from Sina Weibo using a web crawler and cleaned using Pandas. First, Chinese comments regarding the legal sentencing in of Tangshan attack and Jiang Ge Case were segmented and vectorized. Thereafter, a binary LSTM model was trained and tested. Finally, sentiment analysis results were obtained by analyzing the comments with the LSTM model. The accuracy of the proposed model has reached approximately 92%.

The Construction of Campus Network Public Opinion Analysis Model Based on T-GAN Model

The Construction of Campus Network Public Opinion Analysis Model Based on T-GAN Model

Development of Network Public Opinion Analysis System in Big Data Environment Based On Hadoop Architecture

The rapid development of the Internet has given individuals the freedom to fully express their own opinions and value demands, and also accelerated the occurrence and evolution of online public opinions. In essence, online public opinion reflects social conditions and public opinions. In the era of big data, various kinds of data generated by the Internet every day are rapidly growing at the scale of PB level, which poses new challenges to the collation and analysis of online public opinion information. In order to comprehensively improve the authenticity and comprehensiveness of online public opinion governance, realize scientific guidance of public opinion, and optimize the way of online public opinion governance, this paper develops an online public opinion analysis system in the big data environment based on Hadoop architecture, and adopts data mining technology to analyze public opinion in three stages before, during and after the event. It evaluates the risks, predicts the development trend, puts forward early warning in time, and provides theoretical guidance for the governance reform of online public opinions.

A survey of continuous subgraph matching for dynamic graphs

With the rapid development of information technologies, multi-source heterogeneous data has become an open problem, and the data is usually modeled as graphs since the graph structure is able to encode complex relationships among entities. However, in practical applications, such as network security analysis and public opinion analysis over social networks, the structure and the content of graph data are constantly evolving. Therefore, the ability to continuously monitor and detect interesting patterns on massive and dynamic graphs in real-time is crucial for many applications. Recently, a large group of excellent research works has also emerged. Nevertheless, these studies focus on different updates of graphs and apply different subgraph matching algorithms; thus, it is desirable to review these works comprehensively and give a thorough overview. In this paper, we systematically investigate the existing continuous subgraph matching techniques from the aspects of key techniques, representative algorithms, and performance evaluation. Furthermore, the typical applications and challenges of continuous subgraph matching over dynamic graphs, as well as the future development trends, are summarized and prospected.

The System of the Dissemination Characteristics of Internet Public Opinion Big Data Based on Artificial Intelligence

In the era we live in today, the network is often used to analyze a large number of complex systems. With the development of the information society, there are more and more ways to disseminate public information through social networks. Public opinion dissemination refers to the process of disseminating public opinion information through social networks. Because the dissemination of public opinion is the basis for the exchange of ideas among multiple communicators of public opinion, the network community will certainly have an impact on the dissemination and development of public opinion. This article is based on artificial intelligence to study the network public opinion big data dissemination characteristic analysis system, introduces the network public opinion analysis system based on the characteristics of the network public opinion, introduces in detail multiple methods and clustering algorithms for extracting the text information of Internet public opinion, and proposes the Kmeans + Canopy + semantic similarity algorithm, and uses the A event to compare the parameters of the network clustering coefficient, the correlation measure and the degree centrality measure, and the performance of the Kmeans + Canopy algorithm and the Kmeans + Canopy + semantic similarity algorithm. The results of the experiment found that the clustering coefficient of “People’s Daily” in the network dissemination of A event was 0.038, which was the highest among all nodes. It shows that 3.8% of the nodes established by the “People’s Daily” can interact one-to-one to deliver information and intelligence resources. Although the complexity of the algorithm has increased and the time consumed by the system has increased, the accuracy of clustering has been improved, especially for cultural articles, the accuracy rate has been as high as 75%, and entertainment articles can reach up to 70%, and stabilize at around 70%.

Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big Data

The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Community structure detection in a complex network structure and function is used to understand network relations and find its evolution rule; monitoring and forecasting its evolution behavior have an important theoretical significance; in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism, and safeguard national security, it has wide application prospect. A label propagation algorithm is one of the popular algorithms for community detection in recent years; the community detection algorithm based on tags that spread the biggest advantage is the simple algorithm logic, relative to the module of optimization algorithm, convergence speed is very fast, the clustering process without any optimization function, and the initialization before do not need to specify the number of complex network community. However, the algorithm has some problems such as unstable partitioning results and strong randomness. To solve this problem, this paper proposes an unsupervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to find the clustering center, first determines the prototype of the community, and then fixes the number of communities and the clustering center of the complex network, and then uses the label propagation algorithm to detect the community, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the community. Finally, experiments on synthetic network and real network data sets are carried out with the proposed algorithm, and the results show that the proposed method has better performance.

Student Public Opinion Management in Campus Commentary Based on Deep Learning

Aiming at the needs of network public opinion analysis and crisis public opinion early warning in colleges and universities, the semantic sentiment analysis method is studied in this paper. Most of the public opinion information comes from short text comment information, and its text is separated from the written language, the structure becomes simpler, and it lacks normativity, which brings certain difficulties to the extraction of text features. Traditional sentiment analysis methods often rely on emotional dictionaries and feature extraction, and with the continuous change of Internet culture, a technical help is needed to make even the dictionary updated. Based on the analysis and study of attention mechanism and deep learning related technologies, an LSTM model is proposed to mine the deep semantic characteristics of text, which can accurately determine its emotional tendency. The main tasks are as follows: according to the CNN and LSTM text processing, CNN can better extract the local features of the text, and LSTM can retain the text history information and effectively extract the global features of the sequence. The CBOW model is optimized to pay more attention to the feature vectors that affect the classification results during the calculation process. Finally, the improved model in this paper compares the accuracy, recall rate, loss rate, and F1 value of the traditional model to indicate the performance evaluation index of the model.

Research on the Construction of Emergency Network Public Opinion Emotional Dictionary Based on Emotional Feature Extraction Algorithm.

How to strengthen emergency management and improve the ability to prevent and respond to emergencies is an important part of building a harmonious socialist society. This paper proposes a domain emotion dictionary construction method for network public opinion analysis of public emergencies. Using the advantages of corpus and semantic knowledge base, this paper extracts the seed words based on the large-scale network public opinion corpus and combined with the existing emotion dictionary, trains the word vector through the word2vec model in deep learning, expands the emotion words, and obtains the candidate emotion words according to the semantic similarity calculation, So as to generate a domain emotion dictionary. The accuracy rate of emotion discrimination by the emotion dictionary constructed in this paper is 0.86, the recall rate is 0.92. Through the verification of accuracy and recall rate, the construction method proposed in this paper has good accuracy and reliability. Because of the great differences in different experiences and situations of different groups, there will be great differences in views and perspectives on the same event. The key to prevent the public from blindly following the crowd should be to reach groups close to emotional distance, and targeted prevention and control of public opinion can be conducted according to different characteristics of different groups.

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

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.

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), which aims to extract emotions and the corresponding causes in documents, has a wide range of applications in network public opinion analysis. Current two-stage methods first extract emotion and cause clauses, and then pair them. However, there are two problems in these methods: 1) the unidirectional enhancement between emotion and cause extraction fails to make full use of the correlation between them; 2) the errors from the first stage directly degrade the performance of the second stage. To address these problems, we firstly propose a mutually auxiliary multitask model to promote the extraction of emotion and cause clauses by adding two auxiliary tasks which are identical to the original tasks. The proposed model uses the predicted results generated by the two auxiliary tasks as extra features of each other’s main tasks, so as to establish the bidirectional correlation between emotion and cause extraction. Secondly, to reduce the influence of error propagation on the second stage, we design a self-distillation method for pairwise tasks to train the proposed model, which further improve the accuracy of emotion and cause extraction. Experimental results on the ECPE benchmark dataset show that the proposed model has achieved good performance on emotion-cause pair extraction, outperforming the baseline models by 1.92% in F1 score.

An Analysis of the Emotional Evolution of Large-Scale Internet Public Opinion Events Based on the BERT-LDA Hybrid Model

The purpose of this article is to analyse the emotional evolution of the netizens in reaction to the events of the Anti-ELAB (Anti-Extradition Law Amendment Bill) movement in Hong Kong. We attempt to investigate evolving laws of large-scale Internet public opinion events and provide relevant agencies with a theoretical basis for a public opinion response mechanism. On the basis of improving the Bidirectional Encoder Representations from Transformers (BERT) pre-training task, we add in-depth pre-training tasks, and based on the optimisation results of the LDA topic embedding, we integrate deeply with the LDA model to dynamically present the fine-grained public sentiment of the event. Through the collection of large-scale text data related to the Anti-ELAB Movement from a well-known forum in Hong Kong, a BERT-LDA hybrid model for large-scale network public opinion analysis is constructed in a complex context. Through empirical analysis, we calculate and reveal the emotional change process of netizens and opinion leaders in the three transition stages of the Anti-ELAB Movement with the evolution of the topic word as the core by visualisation. We also analyse the emotional distribution and evolution trend of public opinion under the `text topic', and deeply analyse the character and role of opinion leaders in Anti-ELAB public opinion events. The improved BERT-LDA model or sentiment classification AUC value exceeds 99.6% in the sentiment classification task for the Anti-ELAB Movement.

Research Progress of Network Public Opinion Based on Fuzzy Set from the Perspective of Big Data

Granular computing is a new method of intelligent information processing. It describes knowledge and simulates human thinking mode in a granular way. It can realize the rapid transformation between different granular knowledge and is suitable for the analysis and modeling of large-scale and complex data. Fuzzy set is an important model in granular computing, which describes uncertain problems through fuzzy membership function and has been widely used in many fields such as network public opinion analysis. This paper combs the application research of fuzzy sets in network public opinion analysis from the perspective of big data, and discusses the different roles of fuzzy sets in network public opinion analysis from four aspects: fuzzy comprehensive evaluation, fuzzy reasoning, fuzzy granularity and generalized fuzzy set. The characteristics of different methods are compared and the unique features of fuzzy sets in these four aspects are pointed out. The unique advantages and some problems are discussed as well, and the future trend of this field is discussed.

Application of Computer Data Mining Technology in the Analysis of Emotional Law

In recent years, with the in-depth development of the Internet, computer data mining technology has gradually matured, social networks have been integrated into people’s daily lives, and more and more Internet users have expressed their personal emotions and commented on social hotspots on social networks. In this regard, social network public opinion analysis plays a vital role, and has attracted widespread attention from academia and industry. In order to better excavate Weibo users’ opinions and ideas on social events, and track the development of the events, it is more conducive to the work of public opinion monitoring and rumor control. This paper expounds the research status of data mining and the current situation of psychological problems of college students. It introduces computer data mining technology into the analysis of college students’ emotional regularity, and introduces the concepts, functions, technologies, methods and processes of data mining. This paper proposes a clustering method based on the characteristics of the semantic recessive sentiment of frequent itemsets, which fully considers the implicit sentiment regularity of semantics in Weibo. Firstly, we define the frequent feature word set of Weibo based on the characteristics of dominant sentiment, and use the maximum frequent items. Clusters to obtain initial clusters of regular emotions; for text overlap between initial clusters, an inter-cluster overlap reduction algorithm based on short text extended semantic membership is proposed to obtain completely separated initial clusters; based on cluster semantic similarity matrix, agglomerated emotions are given Regular clustering method. Finally, the validity of the text method is verified by the training corpus data provided by the NLP & CC2013 evaluation. It can be known from the test results that when the minimum support degree θ of the cluster is 0.5-0.6, the clustering effect of the analysis of sentiment regularity is better.

Sentiment word co-occurrence and knowledge pair feature extraction based LDA short text clustering algorithm

The Latent Dirichlet Allocation (LDA) topic model is a popular research topic in the field of text mining. In this paper, Sentiment Word Co-occurrence and Knowledge Pair Feature Extraction based LDA Short Text Clustering Algorithm (SKP-LDA) is proposed. A definition of a word bag based on sentiment word co-occurrence is proposed. The co-occurrence of emotional words takes full account of different short texts. Then, the short texts of a microblog are endowed with emotional polarity. Furthermore, the knowledge pairs of topic special words and topic relation words are extracted and inserted into the LDA model for clustering. Thus, semantic information can be found more accurately. Then, the hidden n topics and Top30 special words set of each topic are extracted from the knowledge pair set. Finally, via LDA topic model primary clustering, a Top30 topic special words set is obtained that is clustered by K-means secondary clustering. The clustering center is optimized iteratively. Comparing with JST, LSM, LTM and ELDA, SKP-LDA performs better in terms of Accuracy, Precision, Recall and F-measure. The experimental results show that SKP-LDA reveals better semantic analysis ability and emotional topic clustering effect. It can be applied to the field of micro-blog to improve the accuracy of network public opinion analysis effectively.

User Behavior Prediction of Social Hotspots Based on Multimessage Interaction and Neural Network

In network public-opinion analysis, the diversity of messages under social hot topics plays an important role in user participation behavior. Considering the interactions among multiple messages and the complex user behaviors, this article proposes a prediction model of user participation behavior during multiple messaging of hot social topics. First, considering the influence of multimessage interaction on user participation behavior, a multimessage interaction influence-driving mechanism was proposed to predict user participation behavior more accurately. Second, in the view of the behavioral complexity of users engaging in multimessage hotspots and the simple structure of backpropagation (BP) neural networks (which can map complex nonlinear relationships), this study proposes a user participant behavior prediction model of social hotspots based on a multimessage interaction-driving mechanism and the BP neural network. Finally, the multimessage interaction has an iterative guiding effect on user behavior, which easily causes overfitting of the BP neural network. To avoid this problem, the traditional BP neural network is optimized by a simulated annealing algorithm to further improve the prediction accuracy. In evaluation experiments, the model not only predicted the user participation behavior in actual situations of multimessage interaction but also further quantified the correlations among multiple messages on hot topics.