A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.

Maintainers of Stability: The Labor of China's Data-Driven Governance and Dynamic Zero-COVID

This paper examines the social, technological, and emotional labor of maintaining China's data-driven governance broadly, and dynamic zero-COVID management in particular. Drawing on ethnographic research in China, we examine the sociotechnical work of maintenance during the 2022 Shanghai lockdown. This labor included coordinating mass testing, quarantine, and lockdown procedures as well as implementing ad-hoc technological workarounds and managing public sentiments. We demonstrate that, far from being effected from the top down, China's data-driven governance relies on the circumscribed participation of citizens. During Shanghai's lockdown, citizens with relevant expertise helped to maintain technological stability by fixing or programming data systems, but also to ensure the ongoing production of"positive feelings"about social stability through data-driven governance. In so doing, such citizens simultaneously enacted an ambivalent and circumscribed form of agency, and maintained social and by extension political stability. This article sheds light on data-driven governance and political processes of maintenance. © 2023 ACM.

Internet Governance: Social Mentality and Public Emotion Analysis on Online Media during the COVID-19 Epidemic in Mainland China

Based on a systematical discussion of the logical relationship between social mentality as a psychological basis of social actions and institutions and social governance, and the online emotion as the core element of the dynamic tendency of internet-based social mentality to form emotional energy to promote the operation of the internet society, this paper conducts an empirical study on the online social mentality and public sentiment guidance during the COVID-19 epidemic in mainland China. We use more than 1 million Weibo dynamic data of 104 accounts of three different types including official media, self-media, and big V media and conduct emotional calculation and judgment to address our objectives. The results show that the public sentiment presented by Weibo as the carrier is mainly positive, among which the official media play a positive role in guiding emotions, while the role played by big Vs' is limited during the COVID-19 epidemic. There exists different public sentiment stemmed from the regional differences brought by the heterogeneity of social governance, economic and social development beyond the media guidance. The study provides valuable internet governance experience on how the government can guide the public to respond to and deal with the crisis with a positive attitude when major public health emergencies occur in the future. © 2022 IEEE.

Twitter Data Sentiment Analysis of COVID-19 Vaccination using Machine Learning

The expansion of the web is accelerating, which helps encourage the creation of fresh ideas. In today's internet era, we must suggest techniques to filter out various information. Social media sentiment analysis based on Twitter data can monitor the real-Time monitoring of the COVID-19 vaccine. In this way, relevant organizations or governments can take proactive steps to address misinformation and inappropriate behaviour around the COVID-19 vaccine, which threatens the success of the national vaccination campaign. The purpose of this research is to determine if there is a link between how people feel about the COVID-19 vaccine on Twitter and how many people actually get vaccinated against it. This study uses the COVID-19 All Vaccines Tweet dataset sourced from Kaggle. This research Identifies public sentiment, emotion, word usage, and trend of all filtered tweets. The results show that there are 31% positive tweets, 10% negative tweets, and 58% neutral tweets. Tweets with neutral subjective valence tend to cluster in the middle of the polarity scale (between-1 and +1), whereas tweets with strong subjective valence are spread across the scale. © 2022 IEEE.

Sentiment Analysis towards Face-to-Face School Activities during the COVID-19 pandemic in Indonesia

As the COVID-19 pandemic took place, many face-to-face activities have been stopped to suppress the spread. However, in the last few months, many of those activities including learning activities have started to switch from online back to face-to-face. One of the major activities is face-to-face learning activities which involve millions of students all over Indonesia. Consequently, this study focuses on analyzing public sentiment through Twitter tweets which were obtained through scrapping by using Tweepy. The tweets were labeled using a semi-automatic process, using TextBlob and manual annotation. Next, we trained an IndoBERT model to conduct sentiment analysis and found that public sentiment was dominated by a mix of both negative and positive sentiment, followed by neutral sentiment. Our model obtained an accuracy of 40.79% on unseen data. © 2022 IEEE.

Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

In this analysis, the methods used are the K-Nearest Neighbor classification method and the Logistic Regression classification method with data taken on the twitter application. This study examines the level of accuracy in public sentiment regarding covid-19 vaccination with positive and negative labels. The AUC value in the KNN algorithm with TextBlob labeling is 0.765 with and 0.76S for VaderSentiment labeling are both included in the fair classification criteria. Meanwhile, the Logistic Regression algorithm produces an accuracy of 84.97% with a ratio of 90:10 for Labeling TextBlob, while for Labeling VaderSentiment with a ratio of 90:10 results in an accuracy of 85.22%. Both algorithms are validated using K-Fold Cross Validation with a fold count of 10. The comparison results obtained when conducting an evaluation with the confusion matrix showed that the Logistic Regression algorithm with VaderSentiment labeling had the highest accuracy value compared to the K-Nearest Neighbor algorithm with TextBlob and VaderSentiment labeling. © 2022 IEEE.

The relationship between Twitter sentiment and mobility during the COVID-19 pandemic

The COVID-19 pandemic has affected public behavior in a variety of ways. Concerns about the spread of a hitherto unknown virus drove numerous changes in public behavior, including a greater tendency to self-isolate at home. In this study, we assigned numerical scores to key sentiments expressed in COVID-19-related posts on major social media platform Twitter to measure changes in public sentiment during the pandemic. We also examined the relationship between mobility in various locations around Japan and scores for sentiments such as dislike and fear. Our research provided evidence of a tendency for mobility to decline (i.e. for more people to self-isolate at home) roughly one month after scores for negative public sentiment regarding COVID-19 increased. Mobility is closely connected with a variety of economic activities, mainly in service industries. This suggests that the sentiment in Twitter postings on COVID-19 that we discuss in this study is a leading indicator of changes in mobility (the extent to which people self-isolate at home), demonstrating the effectiveness of Twitter data in forecasting short-term changes in economic activity during the pandemic. © 2022 IEEE.

Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the COVID-19 pandemic.

Background: Public sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management. Objective: This study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management. Methods: This study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic. Results: The research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments. Discussion: Since the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.

Public Sentiment Analysis of Indonesian Tweets About COVID-19 Vaccination Using Different Machine Learning Approaches

More than two years after the start of the coronavirus disease (COVID-19) pandemic, the whole world continues to be impacted by this global crisis. Indonesians use the social media platform Twitter to share information and opinions about coronavirus disease (COVID-19) vaccination. This study was conducted to determine the views of Indonesians toward the government's COVID-19 vaccination program and to test the capability of several machine learning techniques to classify sentiments expressed on Twitter. The performance of four machine learning algorithms was tested: the Naïve Bayes, k-Nearest Neighbors (kNN), Decision Tree, and Support Vector Machine (SVM) algorithms. The findings show that the SVM algorithm exhibited the best performance in terms of accuracy (92%) compared to the Naïve Bayes, kNN, and Decision Tree algorithms. A grid search technique was also used to optimize performance based on parameter settings in the algorithm used. © 2022 IEEE.

Indonesia's Economic Recovery Post Covid-19 Pandemic Sentiment Analysis

Since the Covid-19 pandemic was first confirmed on March 2, 2020, Indonesia has faced many crises, one of which is the economic crisis. Many companies lose profits and impose layoffs for their workers. The Indonesian government in its efforts to restore the economy in Indonesia carried out several maneuvers such as eliminating the PCR/SWAB requirement for public transportation users and increasing tourism enthusiasm in Indonesia by organizing the Mandalika MotoGP. However, this is considered insufficient by some groups of people because the prices of primary needs continue to increase. This study aims to find out public sentiment towards the government for efforts to restore the economy in Indonesia. The results of this study indicate that the Indonesian government is considered successful and has taken the right steps in efforts to recover the economy in Indonesia. This is evidenced by the high percentage margin between positive and negative sentiments of 37%. © 2022 IEEE.

Social Media Mining and Analysis to support authorities in COVID-19 pandemic preparedness

Social media become the main tool for spreading news, discussing ideas and comments on world events. Accordingly, social media represents a precious source to extract insight into public opinion and sentiment. In particular, Twitter has been already recognized as an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The aim of the study reported in this paper is to explore how social media has been exploited to fight COVID-19. In particular, the attention is given on analyzing engagement and interest in the COVID-19 topics and their evolution on a global scale, identifying infodemics, also analysing people feelings and reactions. © 2022 IEEE.

Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

At the end of 2019, the world was hit by the COVID-19 virus, which caused a pandemic. Indonesia has become one of the countries that are affected by this pandemic. To control the COVID-19 pandemic, the government has made various efforts, one of which is the use of the PeduliLindunig app. To access the PeduliLindungi app, the public can download it from Google Play. Google Play enables its users to write reviews on the apps that have been downloaded. This study aims to determine the sentiment analysis on the PeduliLindungi application on Google Play using the Random Forest Algorithm with SMOTE. Based on this study, public sentiment towards the PeduliLindungi app on Google Play tends to be negative. The Random Forest and SMOTE algorithms are used to classify sentiment in this study. The implementation of Random Forest and SMOTE resulted in 71% accuracy, 70% recall, and 70% precision. © 2022 IEEE.

Public Sentiments in User Generated Content amid COVID-19 Pandemic in Ghana

Towards post COVID-19 pandemic, a natural language processing (NLP) technique was leveraged to understand the sentiments of Ghanaians through their public discourse in tweets during the lockdown period in Ghana. With NLP resources, feature words were extracted from the tweets and fed into three machine learning algorithms to track public sentiments in the tweets. The algorithms, support vector machines (SVM), naïve-bayes (NB) and artificial neural network (ANN) were evaluated to ascertain their efficacies. Frequently occurring words used by Ghanaians during the lockdown period were extracted to provide more insight into public sentiments. The study revealed that negative sentiments prevailed throughout the COVID-19 lockdown among Ghanaians. However, positive sentiments were surprisingly high at some points during the lockdown period. The result of evaluating the machine learning classifier yielded SVM as the best performing classifier though the other classifiers performed beyond the acceptable threshold. With these findings, it is envisioned that this study will be adopted by policymakers, as a guide, towards public management of public sentiments in pandemics. © 2022 IST-Africa Institute and Authors.

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets. © 2022 IEEE.

Sentiment Analysis on Covid-19 Vaccinations in Ireland using Support Vector Machine

Monitoring and analyzing social data is currently a norm to gauge public sentiments for efficiently marketing prod-ucts and services. With the recent outbreak of the Coronavirus disease 2019 (Covid-19) and subsequent vaccination programs, it became essential to spread awareness and understand the public sentiments on Covid-19 vaccines. This paper describes the life-cycle of conducting a Sentiment Analysis (SA) on the Covid-19 vaccination program in Ireland. Global and Irish Tweets were collected via Twitter API from January 2020 to August 2021. A lexicon and rule-based VADER tool labelled the global dataset as negative, positive, and neutral. After that, Irish tweets were classified into different sentiments using Support Vector Machine (SVM). Results show positive sentiment toward vaccines at the beginning of the vaccination drive, however, this sentiment gradually changed to negative in early 2021. © 2022 IEEE.

Public Sentiment on User Reviews about Application in Handling COVID-19 using Naive Bayes Method and Support Vector Machine

Another crisis has emerged in the shape of widespread anxiety and panic, fueled by imprecise and frequently incorrect information, during the Coronavirus pandemic. As a result, there is a critical need to address and better comprehend COVID-19's informational crisis, as well as evaluate public mood, in order to adopt effective communications and policy decisions. This study aims to classify the results of PIKOBAR's review sentiments, PIKOBAR is a Center for Information and Coordination of Diseases and Disasters in West Java. A total of 371 reviews were taken, each of which was labeled positive, negative or neutral. The data first goes through a pre-processing process before conducting a sentiment review analysis using the Naive Bayes Classifier and Support Vector Machine processes. The results from 80% testing data and 20% training data obtained the Naive Bayes accuracy rate of 75.67% and the Support Vector Machine was 71.62%. Furthermore, in the text association process, information was obtained that the PIKOBAR application users mostly talked about words 'Jabar' for positive class and the word 'aplikasi' for negative class and the word 'data' for neutral class. © 2022 IEEE.

Twitter Sentiment Analysis of COVID-19 Vaccine Based on BiLSTM with Attention Mechanism

At the end of 2019, COVID-19 started to rage and the vaccine became the most effective solution. the COVID-19 vaccine triggered a high level of attention from social media. twitter became one of the main platforms for the public to post their attitudes and tendencies towards the COVID-19 vaccine, providing a text corpus for public opinion research based on public sentiment analysis. In this paper, we use Twitter posted between August 2020 and August 2021 as the information base. We use the BiLSTM with Attention Mechanism algorithm to calculate the sentiment value of the data, then we use the temporal and spatial dimensions to mine the topics users pay attention to. The results show that positive or negative public sentiment toward vaccines is influenced by external influences (e.g., vaccine safety events, increase in the number of patients receiving vaccines, etc.) and varies by region. The more severe the outbreak (e.g., India, the United States, etc.), the higher the user engagement. The lower the sentiment status and predicted value, the greater the impact on the trend of opinion evolution. In opinion-related networks, the level of engagement with the topic also varies by region and is closely related to the severity of the epidemic, the extent of vaccine promotion and the evolution of topical events. Understanding vaccination sentiment through Twitter can help institutions improve vaccination by keeping abreast of public sentiment about the new crown vaccine. © 2022 IEEE.

Social media and pandemic events: challenges for alert-warning systems

Nowadays, social media platforms generate an immense amount of information in the form of text, images, video, sound, among others. Their capabilities and reliability during adverse situations have made them society's go-to communication method as they continue to operate while more traditional methods fail [1]. With the unexpected arrival of the COVID-19 pandemic, billions of tweets had been generated, bringing both opportunities and challenges to emergency managers when seeking to leverage social media data as a source of information. Therefore, this research investigates how emergency managers could utilize social media data for monitoring public sentiment to enhance their strategic decision-making process. To achieve our end objective, we have adapted a visual analytics framework that has been developed for alerting and monitoring public sentiment during product recalls [2]. The proposed work understands that by developing an alert warning system based on collective sentiment analysis, decision makers will be able to identify scenarios where significant levels of negative or positive sentiment are being disseminated. The alert warning system framework includes concepts on data analytics, natural language processing, and machine learning techniques as mechanisms to generate inferences from social media applications. To illustrate our work, we extracted a sample of 24.7 millions of COVID-19 related tweets from the region of El Paso, TX, which in November 2020 was one of the most dangerous COVID-19 hotspots in the United States [3]. Our results indicate that the adapted framework is an initial step when seeking to assist emergency managers when seeking to utilize social media data;however, it has been found that additional challenges must be addressed before emergency domain decision makers can fully adopt it into their management strategies. © 2022 IEEE.

Analysis of public sentiment tendency in COVID-19 period based on GA-BiLSTM

As the Internet becomes the main source of information for the public, grasping the emotional polarity of online public opinion is particularly important for relevant departments to supervise online public opinion. In order to more accurately determine the emotional polarity of public opinion in the epidemic, this paper proposes a public sentiment analysis model based on Word2vec, genetic algorithm and Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm. The Word2vec model converts the comment text into an n-dimensional vector, uses the Bi-LSTM algorithm to analyze the sentiment polarity, and uses the genetic algorithm to analyze the number of Bi-LSTM layers and the number of fully connected layers and the number of neurons in each layer of Bi-LSTM optimization. The experimental results show that the accuracy of the above model is compared with the accuracy of the Word2vec model and the LSTM model separately, and the accuracy is increased by 11.0% and 7.7%, respectively. © 2022 ACM.

COVID-19 vaccine sentiment analysis using public opinions on Twitter.

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.