Public Opinions on ChatGPT:An Analysis of Reddit Discussions by Using Sentiment Analysis,Topic Modeling,and SWOT Analysis

The sudden arrival of AI(Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

The US–China trade war,the American public opinions and its effects on China

Donald Trump’s trade war with China does not make economic sense,but he does not face much domestic opposition to this trade war.Moreover,it is a part of a broader strategy of the nationalistic Americans’attempt to suppress the rise of China.Would China give in to the requests of the US under the threat of the escalation of the trade war?In what way?My conjecture is that China is willing to compromise up to a point.What China is likely to do is to promise to buy more goods and services from the US,allow greater market access for American firms,reduce Chinese subsidies to its industries,reduce forced technology transfers by American firms,strengthen enforcement of intellectual property rights protection and make verification all these commitments more transparent.Although the US might stop escalating the trade war,it is likely that the tariffs already imposed on Chinese goods would not be removed soon.In response to that,China also would not remove most of those tariffs already imposed on imports from the US,in keeping with the spirit of the tit-for-tat policy.It is possible that a temporary ceasefire is agreed,but the trade war can last for a long time.The final assembly stage of many industries might leave China,but not necessarily the whole production process.Hong Kong can be a victim of the trade war if it escalates.

Entity perception of Two-Step-Matching framework for public opinions

Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public opinions.In this paper,a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities mentioned.Firstly,potential entities are extracted by BiLSTM-CRF model and characteristic words by TF-IDF model from public comments.Secondly,the first matching is implemented between potential entities and an official business directory by Jaro-Winkler distance algorithm.Then,in order to find the pre-cise one,an industry-characteristic dictionary is developed into the second matching process.The precise entity is identified according to the count of characteristic words matching to industry-characteristic dictionary.In addition,associated rate(global indicator)and accuracy rate(sample indicator)are defined for evaluation of matching accuracy.The results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95,averagely enhanced by 32%and 30%above the case of using the first matching process alone.This framework provides the method to find the true target entity of really wanted expression from public opinions.

THE APPLICATION OF COBB-DOUGLAS FUNCTION IN FORECASTING THE DURATION OF INTERNET PUBLIC OPINIONS CAUSED BY THE FAILURE OF PUBLIC POLICIES

Gushes of Internet public opinions may trigger unexpected incidents that significantly affectsocial security and stability, especially for ones caused by the failure of public policies. Therefore,forecasting this kind of Interact public opinions is of great significance. The duration could be citedas one of the most direct indicators that can reflect the severity of a specific Internet public opinioncase. Based on this background, this paper aims to find the factors that may affect the duration of Internet public opinions, and accordingly proposes a model that can accurately predict the durationbefore the release of public policies. Specifically, an index system including 8 factors by consideringfour dimensions, namely, object, environment, reality （offline）, and the network （online）, isestablished. In addition, based on the dataset containing 23 typical Internet public opinion casescaused by the failure of public policies, 9 prediction models are gained by applying the multivariatelinear regression model, multivariate nonlinear regression model, and the Cobb-Douglas function.

Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2(SARSCoV-2)is unprecedented.Little is known,however,about the nuanced public opinions on the vaccines on social media.Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2,classifying them into three groups:pro-vaccine,vaccine-hesitant,and anti-vaccine.After feature inference and opinion mining,10,945 unique Twitter users were included in the study population.Multinomial logistic regression and counterfactual analysis were conducted.Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019(COVID-19)vaccines,either pro-vaccine(B=0.40,SE=0.08,P<0.001,OR=1.49;95%CI=1.26-1.75)or anti-vaccine(B=0.52,SE=0.06,P<0.001,OR=1.69;95%CI=1.49-1.91).People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion(B=−0.18,SE=0.04,P<0.001,OR=0.84;95%CI=0.77-0.90).The United States public is most concerned about the safety,effectiveness,and political issues regarding vaccines for COVID-19,and improving personal pandemic experience increases the vaccine acceptance level.

How Attitudes towards Air Pollution May Impact Public Health: A Case Study of Almaty, Kazakhstan

Using an original public opinion survey, we study public attitudes and behaviors toward air pollution in Almaty, Kazakhstan. In the Health Belief Model (HBM) framework previously used to understand an individual’s health decision-making, we evaluate citizens’ awareness of the poor air quality, their perception of risk, and their willingness to devote time and resources to reduce their air pollution exposure. We find that although citizens are aware of the gravity and general harms of air pollution, they significantly underestimate their individual health risks, and, as a result, often engage in daily routines that exacerbate their exposure to pollution. We find that behaviors increasing the risk of pollution exposure are related to the underlying beliefs about personal health risks, self-efficacy, and material and economic limitations. This means that treating pollution as an individual health problem rather than social issue in public discourse may promote behaviors reducing exposure and improving personal and public health outcomes.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .

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. .

新闻舆论在网络暴力治理中的功能思考——从沃尔特·李普曼的《Public Opinion》谈起

网络暴力通常以言论暴力、舆论极化的形式出现。网络暴力破坏了网络生态,并引起各国的普遍重视。由于新闻媒体议程及其报道框架影响着舆论的形成,新闻舆论对网络舆论的引导也就十分重要。当代人的“媒介化生存”产生了宏大的“拟态环境”,新闻议程对公众议程的塑造日益深重,为权威信源、新闻真相等介入网络舆论治理提供了可能性。主流媒体在场,可以消弭网络空间暴戾之气;权威新闻舆论,可以引导网络空间情感宣泄;发掘新闻真相,能够疏导网络舆论。同时,网络暴力的治理尚需多元力量的参与。

Supervision of Public Opinion under the Background of Social Co-governance-Take the Changsheng Vaccine Incident as an Example

Objective To provide reference for the news media to give play to the role of public opinion supervision in time based on the background of drug safety and social co-governance.Methods The method of case analysis was used to make a retrospective study on the Changsheng vaccine incident in 2018.Then the role of mainstream media,pharmaceutical media,and self-media in the supervision of public opinion was investigated.Results and Conclusion Both mainstream and pharmaceutical media played an excellent role in supervising the Changchun Changsheng vaccine incident.However,the content published by some pharmaceutical media was hard to understand by ordinary people.Besides,the role of self-media in public opinion supervision was polarized.Some self-media closely kept pace with mainstream media in public opinion supervision.Other self-media unilaterally pursued the click rate,publishing false information to guide wrong public opinion.The news media should optimize the supervision efficiency of drug safety.On the one hand,pharmaceutical media should pay attention to the fact that readers may not understand the difficult terms because they are not professional.On the other hand,self-media practitioners should improve their professional quality so that they will not publish some fake news to mislead public opinion.

Research on Risk Prevention and Resolution Strategies of College Network Ideology in the Context of the Internet

With the rapid development of the Internet,the network ideology of colleges and universities is facing severe challenges.This paper deeply analyzes the root of the risk of network ideology and makes a specific investigation of the status quo of network public opinion in colleges and universities.On this basis,the study explores and puts forward a series of targeted risk prevention and resolution strategies,aiming at providing a systematic solution for the network ideology security of colleges and universities.In this paper,with the combination of theory and practice as the path,we verify the effectiveness and applicability of the proposed strategy through the analysis of the implementation effect of the strategy.This study also provides theoretical support and practical guidance for the prevention and control of ideological risks and public opinion guidance in universities under the network environment,which has important practical significance.With the continuous progress of network technology,the threats to the network ideology of colleges and universities are increasing.For example,the spread of false information has become a serious problem affecting the security of college network ideology.

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.

Social bots and mass media manipulated public opinion through dual opinion climate

Opinion dynamics models based on the multi-agent method commonly assume that interactions between individuals in a social network result in changes in their opinions.However,formation of public opinion in a social network is a macroscopic statistical result of opinions of all expressive individuals(corresponding to silent individuals).Therefore,public opinion can be manipulated not only by changing individuals'opinions,but also by changing their states of expression(or silence)which can be interpreted as the phenomenon"spiral of silence"in social psychology.Based on this theory,we establish a"dual opinion climate"model,involving social bots and mass media through a multi-agent method,to describe mechanism for manipulation of public opinion in social networks.We find that both social bots(as local variables)and mass media(as a global variable)can interfere with the formation of public opinion,cause a significant superposition effect when they act in the same direction,and inhibit each other when they act in opposite directions.

Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

SNES: Social-Network-Oriented Public Opinion Monitoring Platform Based on ElasticSearch

With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more important.Many platforms have achieved some success in public opinion monitoring.However,these platforms cannot perform well in scalability,fault tolerance,and real-time performance.In this paper,we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch(SNES).Firstly,SNES integrates the module of distributed crawler cluster,which provides real-time social media data access.Secondly,SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time.Finally,we design subscription module based on Apache Kafka to connect the modules of the platform together in the form of message push and consumption,improving message throughput and the ability of dynamic horizontal scaling.A great number of empirical experiments prove that the platform can adapt well to the social network with highly real-time data and has good performance in public opinion monitoring.

China-Australia Relationship: Australian Public Opinion on China

The latest released Lowy Poll 2019 shows Australia’s public opinion towards China has some negative shifts again.What does this mean for yet-to-be-repaired China-Australia relationship?Does Australian public opinion have an impact on its foreign policy?This article took an analysis of this issue.We found public opinion does have an influence on Australia’s China policy formation through influencing the“popular narrative”on China issue.Nevertheless,the economic ties with China are still fundamental.In the field of foreign policy,the government has a relatively high degree of freedom for direction leading.These facts make the prospect of China-Australia relationship more dependent on the will of current government.While taking care of public opinion is always a first task for them.

Analysis of microblog public opinion characteristics on traditional Chinese medicine against COVID-19 based on deep learning

The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.

Innovation of Ideological and Political Education in Colleges and Universities from the Perspective of Network Public Opinion

In today's era of rapid development of network media,network public opinion has brought great changes to people's lives.As a place of frequent network public opinion,colleges and universities create a more complex and changeable network environment.This is both an opportunity and a challenge to the ideological and political education in universities.Therefore,through a brief introduction of the connotation and characteristics of network public opinion in colleges and universities,this article explores the innovation of ideological and political education in the context of network public opinion in terms of educational concepts,contents,and methods to ensure that this education plays a positive role in the new era.

Internet public opinion monitoring in public health emergencies may benefit from artificial intelligence

Internet public opinion is a summary of all kinds of emotions,attitudes and comments conveying and spreading via the internet.How to guide internet public opinion in times of public health emergencies more efficiently and more effectively has become an urgent issue to be solved.Artificial intelligence helps to monitor the internet public opinion in real time,report automatically,provide personalized guidance,make intelligent predictions and issue early warnings.Artificial intelligence technology completes its missions by building platform frameworks,innovating new types of models and simulating human subjective consciousness and behavior with the aid of big data and integration of machine learning.We believe that using Artificial intelligence technology can effectively enhance guidance,influence and credibility throughout internet.