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Copyright of Nuclear Safety is the property of Nuclear & Radiation Safety Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the popularity of social media, online opinion analysis is becoming more and more widely and deeply used in management studies. Automatically recognizing the sentiment of user reviews is a crucial tool for opinion analysis research. However, previous studies mainly have focused on specific scenarios or algorithms that cannot be directly applied to real-world opinion analysis. To address this issue, we collect a new dataset of user reviews from multiple real-world scenarios such as e-retail, e-commerce, movie reviews, and social media. Due to the heterogeneity and complexity of this multi-scenario review data, we propose a self-distillation contrastive learning method. Specifically, we utilize two EMA (exponential moving average) models to generate soft labels as additional supervision. Additionally, we introduce the prototypical supervised contrastive learning module to reduce the variability of data in different scenarios by pulling in representations of the same class. Our method has proven to be extremely competitive, outperforming other advanced methods. Specifically, our method achieves an 87.44% F1 score, exceeding the performance of current advanced methods by 1.07%. Experimental results, including examples and visualization analysis, further demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

An escalating amount of urban waste poses a significant threat to the sustainable growth of cities. Therefore, a waste-sorting policy is crucial for the advancement of humankind. The implementation of a waste-sorting policy relies heavily on public participation to ensure effective governance. This study examines public participation in urban waste-sorting by mining more than 580,000 microblog texts related to waste-sorting from 2012 to 2022, using data from Sina Weibo, China's leading social media platform. My findings indicate that (1) residents' attention to and support for mandatory waste-sorting policies varies by region; (2) widespread public interest stimulates publicity, education, and commercial entertainment related to waste-sorting policies and investment in thematically related sectors; (3) residents in regions with high education and income levels are more likely to generate waste and pay greater attention to waste-sorting policies; and (4) the promotion of waste-sorting policies affects knowledge dissemination. This study combines textual analysis and econometric techniques to offer a fresh perspective on the significance of public participation in promoting waste-sorting policies. It serves as an invaluable resource for governments to implement waste-sorting policies. [ABSTRACT FROM AUTHOR]

Featured Application: Public Opinion Analysis. The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public's opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art. [ABSTRACT FROM AUTHOR]

An innovative approach is introduced in this paper to address the challenges in emotional topic interpretation and accuracy in emotional situation assessment. Utilizing large data from social media to improve the accuracy of emotional analysis in online debates, with a specific emphasis on Korean themes. The proposed solution, the Online Topic Emotion Recognition Model (OTSRM), builds upon the foundational Online Latent Dirichlet Allocation (OLDA) model. The OTSRM integrates the concept of emotion intensity and introduces an inventive emotion iteration framework to tackle these issues. Key innovations of the OTSRM include establishing an affective evolution channel by augmenting affective heritability using a ß priori. Additionally, the model generates two critical distribution matrices: one for characteristic words and another for affective words, facilitating a deeper understanding of emotional context within topics. The relative entropy method is employed to discern emotional tones in textual content, calculating maximum emotion values for topic focus within adjacent time segments. Validation experiments using five diverse network event datasets and comparisons to mainstream models demonstrate the OTSRM's effectiveness with emotion recognition accuracy rates of 85.56% and 81.03%. The OTSRM represents significant progress in addressing challenges associated with emotional topic analysis and precise emotional dynamics assessment in Korean social media data. [ABSTRACT FROM AUTHOR]

As the center of the financial market, the stock market is popular with the public attention of investors. It is of great significance for investors that an effective analytic method of stock public opinion is proposed. As the main communication platform, the forum not only provides the investors with investment information but also comments related to the stock market. In view of the defects of text emotions and investment problems, this paper proposes a framework based on web crawler and deep learning technologies including one-dimensional convolutional neural networks (1DCNN) and long short-term memory (LSTM), to evaluate the stock market volatility. Among them, the extracted features include not only the stock price but also the text information. Firstly, we develop the crawler technology to grab large-scale text data from the internet and they are manually labeled their emotions by analyzing the relevant financial knowledge. Secondly, as the character-level text classification method, the 1DCNN is designed for text sentiment classification to detect the reliability of text annotation. Finally, considering the time sequence of price and the continuity of post influence, the emotional and technical features are combined to estimate the fluctuation of the stock market in different industries by the LSTM model. We test four evaluation indexes, the classification accuracy of the model is 74.38%, the accuracy rate is 76.83%, the recall rate is 70%, and the F1 value is 72.8%. The results show that the combination of characteristics of internet public opinion more effectively evaluates the changes in the stock market. [ABSTRACT FROM AUTHOR]

Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of "topic-time" in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the "topic-word" distribution is semantically aggregated within various topics, and "topic-time" distribution clustered within a time window under each topic. Furthermore, the characteristics of the trend of each topic over time are basically consistent with the corresponding trend of topic in reality in terms of content. These indicate that the DMMOT model improves topic clustering for short text to some extent. Furthermore, the DMMOT model performed well in both temporal and spatial analysis of public opinion topics based on microblog data. [ABSTRACT FROM AUTHOR]

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains. [ABSTRACT FROM AUTHOR]

Natural disasters are usually sudden and unpredictable, so it is too difficult to infer them. Reducing the impact of sudden natural disasters on the economy and society is a very effective method to control public opinion about disasters and reconstruct them after disasters through social media. Thus, we propose a public sentiment feature extraction method by social media transmission to realize the intelligent analysis of natural disaster public opinion. Firstly, we offer a public opinion analysis method based on emotional features, which uses feature extraction and Transformer technology to perceive the sentiment in public opinion samples. Then, the extracted features are used to identify the public emotions intelligently, and the collection of public emotions in natural disasters is realized. Finally, through the collected emotional information, the public’s demands and needs in natural disasters are obtained, and the natural disaster public opinion analysis system based on social media communication is realized. Experiments demonstrate that our algorithm can identify the category of public opinion on natural disasters with an accuracy of 90.54%. In addition, our natural disaster public opinion analysis system can deconstruct the current situation of natural disasters from point to point and grasp the disaster situation in real-time.

Public opinion is the abbreviation of public ideas, which is the sum of the beliefs, attitudes, opinions and emotions expressed by the masses about various phenomena in the society. Network public opinion is not only the mapping of public opinion in the network, but also the direct response of social public opinion. Weibo public opinion has gradually become an important medium for people to obtain public opinion in time. The outbreak of Weibo public opinion is very likely to cause social repercussions, which will even affect people's trust in the government and even social stability. The scale of the Internet is expanding, the number of users is increasing, and the applications provided by the Internet are becoming more and more popular. In this context, Weibo public opinion analysis has become an important research topic. Facing the rapid growth of micro blog information, how to obtain the hot topics of micro blog public opinion timely, comprehensively and accurately is the primary problem to be solved in the process of micro blog public opinion analysis. Based on the infectious disease model, this paper proposes an analysis model of Weibo public opinion communication. The speech analysis model is proposed to extract the information for the processing, and the representation of the data is applied to improve the algorithm efficiency. The experimental results compared with the state-of-the-art have proven the satisfactory performance. [ABSTRACT FROM AUTHOR]

Copyright of Food Science & Technology & Economy is the property of Grain Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasingly intensified international competition in science and technology and rapidly development of science and technology in China, the national science image has increasingly become an important part of the whole national image that cannot be ignored. Through the analysis of discussions on Chinese science-related topics on Twitter, the international social media platform, it is found that the Western public's current attention to Chinese science-related topics is not high, while it continues to grow. The discussions around Chinese science-related topics are dominated by a small number of major scientific events, and are obviously guided and controlled by the power of mainstream Western news media and political discourse, and the main cognition is biased towards the negative. Moreover, the construction of such negative image of Chinese science has been involved in the political discourse and international relations to a certain extent, characterized by concerns about reports of negative Chinese tech news and conspiracy theories. The positive image of Chinese science is more characterized by the appreciation of China's breakthrough scientific achievements and scientists who have won international scientific awards and the special attention to Chinese science fiction in the context of "depoliticization". In this regard, to enhance China's international science image, it is necessary to formulate targeted international science and technology communication strategies based on paying attention to the social media platform as a public opinion field and combining the research results. [ABSTRACT FROM AUTHOR]

In the era of mobile Internet, college students increasingly tend to express their opinions and views through online social media; furthermore, social media influence the value judgments of college students. Therefore, it is vital to understand and analyze university online public opinion over time. In this paper, we propose a data-driven architecture for analysis of university online public opinion. Weibo, WeChat, Douyin, Zhihu and Toutiao apps are selected as sources for collection of public opinion data. Crawler technology is utilized to automatically obtain user data about target topics to form a database. To avoid the drawbacks of traditional methods, such as sentiment lexicon and machine learning, which rely on a priori knowledge and complex handcrafted features, the Word2Vec tool is used to perform word embedding, the LSTM-CFR model is proposed to realize Chinese word segmentation and a convolutional neural network (CNN) is built to automatically extract implicit features in word vectors, ultimately establishing the nonlinear relationships between implicit features and the sentiment tendency of university public opinion. The experimental results show that the proposed model is more accurate than SVM, RF, NBC and GMM methods, providing valuable information with respect to public opinion management. [ABSTRACT FROM AUTHOR]

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

Public opinion guidance plays a crucial role in the management of major public health incidents, and thus, exploring its mechanism is conducive to the comprehensive governance of social security. This study conducts a case study on the anti-pandemic public opinion guidance and analyzes the public opinion representation and the internal mechanism of public opinion guidance in the context of the COVID-19 in China. The findings suggest that the public opinion on the COVID-19 manifested a three-stage progressive and stable tendency and witnessed the strength of China, specifically, benefiting from the systematic and complete integration and release mechanism for anti-pandemic information, the three-dimensional mechanism for the dissemination of knowledge related to pandemic prevention and health, the innovative disclosure mechanism for precise information, and diversified channels for international public opinion guidance. The guidance mechanism proposed in this study provides significant suggestions for the public opinion guidance of global major public health incidents in future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Novel coronavirus pneumonia (NCP) is an acute infectious pneumonia. The 2019 novel coronavirus, which is highly infectious, and has been rapidly spreading to the world by the end of 2019 and beginning of 2020. Countries and regions, causing serious psychological panic among the people in the epidemic area, so this article uses crawler to obtain novel coronavirus post bar data for public opinion analysis, the purpose of which is to quickly obtain a large number of reliable and complete information data to analyze their mental health and This makes a response strategy, with a view to promoting the research and development of psychological health intervention in the affected areas.

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Summary: Aspect‐category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect‐category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect‐category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra‐graph convolution and inter‐graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models. [ABSTRACT FROM AUTHOR]

With the worldwide proliferation of social networks, public opinion analysis of data generated by social networks has become an important field of research. Social networks have become a major platform for public opinion formation and diffusion, and analyzing public opinion through social network data plays an important role across numerous fields, including political science, economics, commerce, finance, international trade, public policy implementation and so on. Nevertheless, the corresponding quantitative indexes of public opinion analysis have not yet been developed, and the theoretical foundation underpinning such indexes has yet to be established. How to measure public opinion through social network data is a significant problem in need of the development of a series of quantitative assessment indices and social computing methods that can be used to solve this problem. This paper proposes both the concept of a public psychological pressure index and its calculation method, making it a fundamental work in the field of public opinion analysis. The maximum entropy principle is introduced to the social computing domain in this paper and positions it as the theoretical foundation underpinning such indexes.

In the era of social media, the political speech of undergraduates blots out the sky and covers up the earth, which perhaps generates the negative effect. To enhance the management ability of government departments, it is considerably necessary to pay more attention to the public opinion. However, the current researches on the public opinion mainly focus on mathematical modeling or data mining irrespective of public opinion analysis. Consider that Convolutional Neural Network (CNN) has the strong data analysis ability, this paper uses CNN to realize the special application, that is, politics public opinion analysis of undergraduates, which has two functions. On one hand, greatly help the government departments eliminate the crisis timely; On the other hand, correctly guide the political education of undergraduates. Besides, this paper also presents a case study based on Content Delivery Network (CDN) deployment, in which a monitor system of public opinion analysis is implemented to analyze the undergraduates' political speech. Finally, with three public opinion dissemination modes consideration, the experiments are made. The results show that CNN has better training ability and the whole deployment is more significant compared to the state‐of‐the‐art schemes. [ABSTRACT FROM AUTHOR]

In the era of mobile Internet, college students increasingly tend to express their opinions and views through online social media; furthermore, social media influence the value judgments of college students. Therefore, it is vital to understand and analyze university online public opinion over time. In this paper, we propose a data-driven architecture for analysis of university online public opinion. Weibo, WeChat, Douyin, Zhihu and Toutiao apps are selected as sources for collection of public opinion data. Crawler technology is utilized to automatically obtain user data about target topics to form a database. To avoid the drawbacks of traditional methods, such as sentiment lexicon and machine learning, which rely on a priori knowledge and complex handcrafted features, the Word2Vec tool is used to perform word embedding, the LSTM-CFR model is proposed to realize Chinese word segmentation and a convolutional neural network (CNN) is built to automatically extract implicit features in word vectors, ultimately establishing the nonlinear relationships between implicit features and the sentiment tendency of university public opinion. The experimental results show that the proposed model is more accurate than SVM, RF, NBC and GMM methods, providing valuable information with respect to public opinion management.

Correlation analysis of law-related news is a task to of dividing news into law-related or law-unrelated news, which is the basis of public opinion analysis. Public opinion news consists of the title and the body. The title describes the theme of the news, and the body describes the content of the news. They are equally important and interdependent in the analysis of lawrelated news. Therefore, we make full use of the dependence between the title and the body and propose a learning method that combines the bidirectional attention flow of the title and the body. This method encodes the title and the body respectively by using a bidirectional gated recurrent unit (BiGRU) to obtain the word-level feature matrix of the title and the word-level feature matrix of the body. Then it further extracts the law relevant key features from the body feature matrix, to obtain the word-level feature representation of the body. Finally, we combine the word-level feature representation of the title and the body to build bidirectional attention flow. In this way, the information of the two is fully integrated and interacted to improve the accuracy of the legal correlation analysis of news. To verify the validity of the method in this paper, we conducted experiments on the analysis of law-related news. The results show that our method has achieved good results. Compared with the baseline method, the F1 values of our method is increased by 2.2%, which strongly proves that the interaction between title and body has a good supporting effect on news text classification. [ABSTRACT FROM AUTHOR]

As innumerable events are reported on Weibo every day, it becomes especially important to predicate the event development trends early as possible. Weibo has been indispensable to the public life; its topic heat predication has become one of the hotspot subjects in data excavation for it provides the basis for public opinion monitoring. In this paper, a non-parametric method is adopted to predicate the heat variation of topic. This method, while maintaining the lower error rate, can effectively predicate whether the topic is heating up. Through the experiment, the parameters can be set flexibly to balance the detection time, true positive rate and false positive rate. The algorithm proposed in this research is effective and extendible, it is believed to help the Sina weibo developer and government in public opinion monitoring. According to this requirement, a non-parametric method is proposed to predicate the development trend of Sina weibo topics. [ABSTRACT FROM AUTHOR]

Online public opinion reflects social conditions and public attitudes regarding special social events. Therefore, analyzing the temporal and spatial distributions of online public opinion topics can contribute to understanding issues of public concern, grasping and guiding the developing trend of public opinion. However, how to evaluate the validity of classification of online public opinion remains a challenging task in the topic mining field. By combining a Bidirectional Encoder Representations from Transformers (BERT) pre-training model with the Latent Dirichlet Allocation (LDA) topic model, we propose an evaluation method to determine the optimal classification number of topics from the perspective of semantic similarity. The effectiveness of the proposed method was verified based on the standard Chinese corpus THUCNews. Taking Coronavirus Disease 2019 (COVID-19)-related geotagged posts on Weibo in Wuhan city as an example, we used the proposed method to generate five categories of public opinion topics. Combining spatial and temporal information with the classification results, we analyze the spatial and temporal distribution patterns of the five optimal public opinion topics, which are found to be consistent with the epidemic development, demonstrating the feasibility of our method when applied to practical cases.

Typhoon disaster may not only cause power outage physically, but also breed negative sentiments in public opinion and affect social stability. Therefore, an intelligent power grid allocation technology considering secondary disaster and public opinion under typhoon disaster is proposed. Firstly, an Extra-Tree (ET) method is used to predict the power outage in time sequence (e.g., landing period, 6 h later, and 12 h later) after typhoon lands. Secondly, a typhoon secondary disaster potential assessment model is established based on disaster intensity and geographical environment. Since power outages may also cause public opinion polarization, a public opinion analysis model is proposed to mine Micro-blog (similar to Twitter) tweet information and analyze the demand for power restoration. Then, according to the results of secondary disaster potential assessment and public opinion analysis, the allocation strategy optimization is described as a Mixed Integer Nonlinear Programming (MINLP) problem. It is solved by the Stud Genetic Algorithm (Stud GA) and A-star path optimization algorithm. Finally, Yangjiang city in China under Typhoon "Chaba" (2022) is selected as the study area to verify the effectiveness and feasibility. It shows the proposed method can respond to public electricity demand in time and eliminate public negative sentiments, while the repair process is not disturbed by secondary disasters. • A secondary disaster evaluation model is proposed to prevent allocation delay. • Analyzing public opinion to reflect restore demand and alleviate negative sentiment. • A time-sequential outage prediction model is proposed to get higher accuracy. • A MINLP allocation strategy with typhoon "Chaba" (2022) data and opinion analysis. [ABSTRACT FROM AUTHOR]

Abstract In recent years, researches on sentiment of individual investors are becoming popular, especially using big data technology. Most researches mainly focus on the relationship between the individual sentiment and the market fluctuations. In this work, we aim to testify the time-sensitivity of stock prices against the general sentiment of individual investors and to assess the influence of the evaluation of consulting institutions on stock prices. Firstly, we develop a distributed web spider to fetch the opinions data from social networks like Sina Guba, Eastmoney Guba and Sina Finance, and financial data from the RESSET database. Then through natural language processing, we build a sentiment index and institution evaluation index. Finally, we use correlation analysis, regression analysis and time series analysis to achieve our targets. The results show that: 1) the fluctuation of stock prices is more sensitively to the intraday sentiment of individuals; 2) there is no significant correlation between general sentiment of individual investors and the evaluation of consulting institutions, though the two factors both surely influence the change of stock prices; 3) the change of stock prices can be expressed by the public opinion well. [ABSTRACT FROM AUTHOR]

With the worldwide application of social networks, new mathematical approaches have been developed that quantitatively address this online trend, including the concept of social computing. The analysis of data generated by social networks has become a new field of research; social conflicts on social networks occur frequently on the internet, and data regarding social behavior on social networks must be analyzed objectively. In this paper a type of social compouting method based on the principle of maximum entropyis proposed, and this type of social computing method can solve a series of complex social computing problems including the calculation of public psychological pressure. The quantitative calculation of public psychological pressure is so important to the public opinion analysis that it can be widely applied in a lot of public information analysis fields. [ABSTRACT FROM AUTHOR]

With the worldwide proliferation of social networks, the social networks have played an important role in the social activities .Peoples are inclined to obtain the corresponding public opinion to make decision such as shopping, education, investment and so on. Analysis of data generated by social networks has become an important field of research, however in the field of public opinion analysis of social networks the quantitative measure indexes are still lacking. In this paper, the calculation method of pessimistic investor sentiments indicator is proposed, and the index has a certain theoretical and practical value. [ABSTRACT FROM AUTHOR]

• We propose an opinion-transfer susceptible-forwarding-immunized (OT-SFI) propagation model, taking into consideration of co-propagation by two pieces of information with the same or opposite opinions on the topic released sequentially. • Our model devides population into six states, the susceptible state S (t) , the immune state I (t) , the state where people forward the first information i with positive opinions F Pos i t or negative opinions F Neg i t , and the state where people forward the following information j with positive opinions F Pos j t or negative opinions F Neg j t . • After parameter estimations, it is found that a large portion of them tend to forward the information with the opinion they agree with. • Effective communication strategies are identified to redirect public opinions and reduce the spread of rumors. On social media platforms, hot topics often contain several pieces of related information that can influence internet users, generating either positive or negative opinion orientation. Some of them will choose to retain or change their original opinions after exposure to multiple related messages. To describe the opinion-transfer transient and collective behaviors in this scenario, this paper proposes an opinion-transfer susceptible-forwarding-immunized (OT-SFI) information cross-propagation model. Real multiple information in messages with opinions obtained from the Chinese Sina microblog is used for data fitting to illustrate how model parameters can be estimated and used to predict the accumulative numbers of users with a particular view. The study attempts to relate changes in group views in the network to initial opinion distribution and individuals' opinion choices at the macro level. Furthermore, the model parameters at the micro level are used to measure the probability of "retention" and "reversal" of views in events, as well as the extent to which the masses are influenced by new information views. The result illustrates that the viewpoint distribution of the initial message and the opinion selection of the new message opinion leaders play crucial roles in promoting attention to the topic and driving for a desired collective opinion. [ABSTRACT FROM AUTHOR]

Trust in social network draws more and more attentions from both the academia and industry fields. Public opinion analysis is a direct way to increase the trust in social network. Because the public opinion analysis can be expressed naturally by the graph algorithm and graph data are the default data organization mechanism used in large-scale social network service applications, more and more research works apply the graph processing system to deal with the public opinion analysis. As the data volume is growing rapidly, the distributed graph systems are introduced to process the large-scale public opinion analysis. Most of graph algorithms introduce a large number of data iterations, so the synchronization requirements between successive iterations can severely jeopardize the effectiveness of parallel operations, which makes the data aggregation and analysis operations become slower. In this paper, we propose a large-scale graph data processing system to address these issues, which includes a graph data processing model, Arbor. Arbor develops a new graph data organization format to represent the social relationship, and the format can not only save storage space but also accelerate graph data processing operations. Furthermore, Arbor substitutes time-constrained synchronization operations with non-time-constrained control message transmissions to increase the degree of parallelism. Based on the system, we put forward two most frequently used graph applications on Arbor: shortest path and PageRank. In order to evaluate the system, we compare Arbor with the other graph processing systems using large-scale experimental graph data, and the results show that it outperforms the state-of-the-art systems. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper designs a system of network public opinion analysis based on the specific way of “matching Topic with Opinion” which can organize public opinion data, avoid the redundant data and retain the original information structure of the opinion. And this article proposes a user model founded on the user access behavior to scientifically classify and represent the relevant theory of the users’ retrieval behavior, then to analyze and manage the retrieval results which can to provide the accurate and relevant search results of public opinion information for users. [ABSTRACT FROM AUTHOR]

This article carried on the public opinion analysis and the empirical investigation to the Hangzhou square dance by questionnaire survey, telephone interview, mathematical statistics and inductive deduction method, then analyzed the existing problems, raised practical countermeasures and suggestions in order to realize the rational sustainable development of square dance, achieve the harmonious state of national fitness and health and provide a reference sample to the construction of public cultural services. [ABSTRACT FROM AUTHOR]

For the problem that current research on public opinion analysis in micro-blog network does not consider public opinion text features from the global level, a public opinion analysis model based on topic detection is presented according to micro-blog network public opinion topic and trend analysis. The model is described from three aspects, text preprocessing,micro-blog text feature extraction, topic detection and trend analysis. The simulation results show that the detection performance of the model is satisfying and the model is effective in the analysis of micro-blog network public opinion. This exploration in this paper can provide some valuable reference for further research in this field. [ABSTRACT FROM AUTHOR]

The present study analyses the controversies around surveys and exit polls that were conducted during the last election campaigns in Romania. Three aspects emerge following the analysis. First issue is the competition for the symbolic construction of the political field, competition in which the surveys’ discourse collides with those of the politicians and the media. Secondly, the interaction between the interviewer and the respondent could be regarded as similar to a process of symbolic violence, which may generate distortions. Thirdly, the study also advances theoretical adjustments concerning public opinion analysis and the survey practice, from the point of view of a discursive, interactionist communication paradigm, within which the survey is seen as a scene for interaction, and, if it touches on a controversial issue, could cause the genesis of certain public opinion elements and could lead to the gathering of an audience. The author asserts that individuals have a certain mental representation of the common opinion and that they adjust their own opinions according to what they believe the common one is. The purpose of the climate of opinion is not that of social regulation or of social control; public opinion is a mechanism of mutual adjustment that enables people to be aware of and to take into account what other people are thinking. Surveys capture fragments pertaining to regular citizens’ discourses; these ordinary citizens are not content with applying interpretative frames conveyed by the media, they mobilize their own experiences and interpersonal discussions in order to negotiate the meaning of political issues.