Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.
RCC Institute of Information Technology（RCC信息技术学院）；Maulana Abul Kalam Azad University of Technology（毛拉纳·阿布·卡拉姆·阿扎德技术大学）
As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lock-down to check its intensity. During this lockdown period, social media plat-forms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into ac-count the popular words either directly or indirectly based on this pa…
Nanjing University of Science and Technology（南京理工大学）；Dalhousie University（达尔豪斯大学）；Jiangsu University of Science and Technology（江苏科技大学）
Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language proces…
University of Ottawa（渥太华大学）；Dalhousie University（达尔豪斯大学）
Reddit.com is a popular social media platform among young people. Reddit users share their stories to seek support from other users, especially during the Covid-19 pandemic. Messages posted on Reddit and their content have provided researchers with opportunity to analyze public concerns. In this study, we analyzed sentiments of COVID-related messages posted on r/Depression. Our study poses the following questions: a) What are the common topics that the Reddit users discuss? b) Can we use these topics to classify sentiments of the posts? c) What matters concern people more during the pandemic? …
The novel coronavirus (COVID-19) pandemic is the most talked topic in social media platforms in 2020. People are using social media such as Twitter to express their opinion and share information on a number of issues related to the COVID-19 in this stay at home order. In this paper, we investigate the sentiment and emotion of peoples in the United States on the subject of reopening. We choose the social media platform Twitter for our analysis and study the Tweets to discover the sentimental perspective, emotional perspective, and triggering words towards the reopening. During this COVID-19 pan…
University of Ottawa（渥太华大学）；Dalhousie University（达尔豪斯大学）
This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER and TextBlob to label messages for Machine Learning experiments. Our results show that removal of shortest and longest messages improves VADER and TextBlob agreement on positive sentiments and F-score of sentiment classification by all the three algorithms
St. Luke's Medical Center - College of Medicine（圣卢克医疗中心医学院）
At the peak of the COVID-19 pandemic, numerous countries worldwide sought to mobilize vaccination campaigns in an attempt to curb the spread and number of deaths caused by the virus. One avenue in which information regarding COVID vaccinations is propagated is that of scientific articles, which provide a certain level of credibility regarding this. Hence, this increases the probability that people who view these articles would get vaccinated if the articles convey a positive message on vaccinations and conversely decreases the probability of vaccinations if the articles convey a negative messa…
The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper we examine the role of social messaging shared by Persons in the Public Eye (i.e. athletes, politicians, news personnel) in determining overall public discourse direction. We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet…
Nanyang Technological University（南洋理工大学）；IBM Research Australia（IBM澳大利亚研究院）
Situated in the global outbreak of COVID-19, our study enriches the discussion concerning the emergent racism and xenophobia on social media. With big data extracted from Twitter, we focus on the analysis of negative sentiment reflected in tweets marked with racist hashtags, as racism and xenophobia are more likely to be delivered via the negative sentiment. Especially, we propose a stage-based approach to capture how the negative sentiment changes along with the three development stages of COVID-19, under which it transformed from a domestic epidemic into an international public health emerge…
Covid-19 has spread across the world and several vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pre-trained transformer models on tweets associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art pre-trained transformer models RoBERTa, XLNet and BERT, and the domain-specific transformer models CT-BERT and BERTweet that are pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using L…
University of New South Wales（新南威尔士大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks…
University of Birmingham（伯明翰大学）；De La Salle University（德拉萨大学）
Vaccine hesitancy and other COVID-19-related concerns and complaints in the Philippines are evident on social media. It is important to identify these different topics and sentiments in order to gauge public opinion, use the insights to develop policies, and make necessary adjustments or actions to improve public image and reputation of the administering agency and the COVID-19 vaccines themselves. This paper proposes a semi-supervised machine learning pipeline to perform topic modeling, sentiment analysis, and an analysis of vaccine brand reputation to obtain an in-depth understanding of nati…
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Aust…
During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the first European countries to be severely affected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using different lexicons-based methods, we find a breakpoint corresponding to the date of the first established …
Social media is an appropriate source for analyzing public attitudes towards the COVID-19 vaccine and various brands. Nevertheless, there are few relevant studies. In the research, we collected tweet posts by the UK and US residents from the Twitter API during the pandemic and designed experiments to answer three main questions concerning vaccination. To get the dominant sentiment of the civics, we performed sentiment analysis by VADER and proposed a new method that can count the individual's influence. This allows us to go a step further in sentiment analysis and explain some of the fluctuati…
Understanding the public sentiment and perception in a healthcare crisis is essential for developing appropriate crisis management techniques. While some studies have used Twitter data for predictive modelling during COVID-19, fine-grained sentiment analysis of the opinion of people on social media during this pandemic has not yet been done. In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. For this purpose, we perform supervised training of four transformer language models on the downstream task of multi-label classification of tweets into seven ton…
University of Toronto（多伦多大学）；University of Pittsburgh（匹兹堡大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities…
College of Science and Engineering, HBKU（哈马德·本·哈利法大学科学与工程学院）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Information Technology University（信息技术大学）；University of Nebraska（内布拉斯加大学）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；University of Trento（特伦托大学）；Shaheed Benazir Bhutto Women University（贝娜齐尔·布托女子大学）
Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing informat…
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets
University of Birjand（比尔詹德大学）；Dibrugarh University（迪布鲁加尔大学）；Fasa University（法萨大学）；Ferdowsi University of Mashhad（马什菲尔德大学）；Deakin University（迪肯大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Khulna University（库尔纳大学）
Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee at Knoxville（田纳西大学诺克斯维尔分校布雷德森跨学科研究与研究生教育中心）；Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center（田纳西大学健康科学中心医学院儿科系生物医学信息学中心）；Hubert Department of Global Health, Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院休伯特全球卫生系）
This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter, harvested from January 1, 2020, to March 1, 2022. To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9.5 million Tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 Tweets and then augmented our dataset by the method of back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model u…
The words we use to talk about the current epidemiological crisis on social media can inform us on how we are conceptualizing the pandemic and how we are reacting to its development. This paper provides an extensive explorative analysis of how the discourse about Covid-19 reported on Twitter changes through time, focusing on the first wave of this pandemic. Based on an extensive corpus of tweets (produced between 20th March and 1st July 2020) first we show how the topics associated with the development of the pandemic changed through time, using topic modeling. Second, we show how the sentimen…
University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse, the emergence of the COVID-19 Delta variant once again puts the world in danger. Fortunately, many countries and companies have started to develop coronavirus vaccines since the beginning of this disaster. Till now, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The promotion of COVID-19 vaccination around the world also brings a lot…
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review
University of São Paulo（圣保罗大学）；University of Birmingham（伯明翰大学）；University of Rio de Janeiro State（里约热内卢州立大学）
The Coronavirus pandemic has affected the normal course of life. People around the world have taken to social media to express their opinions and general emotions regarding this phenomenon that has taken over the world by storm. The social networking site, Twitter showed an unprecedented increase in tweets related to the novel Coronavirus in a very short span of time. This paper presents the global sentiment analysis of tweets related to Coronavirus and how the sentiment of people in different countries has changed over time. Furthermore, to determine the impact of Coronavirus on daily aspects…
North South University（北南大学）；Taif University（塔伊夫大学）；Princess Sumaya University for Technology（苏玛雅公主科技大学）
This COVID-19 pandemic is so dreadful that it leads to severe anxiety, phobias, and complicated feelings or emotions. Even after vaccination against Coronavirus has been initiated, people feelings have become more diverse and complex, and our goal is to understand and unravel their sentiments in this research using some Deep Learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of it, specifically Twitter, one can have a better idea of what is trending and what is going on in people minds. Our motivation for this research is to understa…
Norwegian University of Science & Technology（挪威科技大学）；Sukkur IBA University（苏库尔IBA大学）；Linnaeus University（林奈大学）
How different cultures react and respond given a crisis is predominant in a society's norms and political will to combat the situation. Often the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the will of the nation. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, …
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology
Indian Institute of Information Technology, Nagpur（印度信息技术学院，那格浦尔）；Shri Ramdeobaba College of Engineering & Management, Nagpur（什里·拉姆德奥巴巴工程与管理学院，那格浦尔）；Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore（德维·阿希利亚大学工程与技术学院，印多尔）
School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；School of Computer Science and Cyberspace Security, Hainan University（海南大学计算机与网络空间安全学院）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Defense Technology（国防科技大学）；Department of Mathematics and Computer Science, Northeastern State University（东北州立大学数学与计算机科学系）
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 is proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the re…
School of Journalism and Communication, Tsinghua University（清华大学新闻与传播学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-lev…
Background: Neural networks produce biased classification results due to correlation bias (they learn correlations between their inputs and outputs to classify samples, even when those correlations do not represent cause-and-effect relationships). Objective: This study introduces a fully unsupervised method of mitigating correlation bias, demonstrated with sentiment classification on COVID-19 social media data. Methods: Correlation bias in sentiment classification often arises in conversations about controversial topics. Therefore, this study uses adversarial learning to contrast clusters base…
University of British Columbia（不列颠哥伦比亚大学）；British Columbia Centre for Disease Control（不列颠哥伦比亚省疾病控制中心）
Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has significantly impacted on people's lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implement…
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney（过渡人工智能研究组，数学与统计学学院，UNSW悉尼）；Centre for Artificial Intelligence and Innovation, Pingla Institute, Sydney, Australia（人工智能与创新中心，Pingla研究所，悉尼，澳大利亚）；Department of Data Science and Artificial Intelligence, IIIT Naya Raipur（数据科学与人工智能系，IIIT奈亚拉普尔）
University of Birjand（比尔詹德大学）；Birjand University of Technology（比尔詹德理工大学）
The Covid-19 virus has been one of the most discussed topics on social networks in 2020 and 2021 and has affected the classic educational paradigm, worldwide. In this research, many tweets related to the Covid-19 virus and education are considered and geo-tagged with the help of the GeoNames geographic database, which contains a large number of place names. To detect the feeling of users, sentiment analysis is performed using the RoBERTa language-based model. Finally, we obtain the trends of frequency of total, positive, and negative tweets for countries with a high number of Covid-19 confirme…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining. To develop COVID-19Base, we mine the information from publicly available scientific literature and related public resources. We considered seven topic-specific dictionaries, including human genes, human miRNAs, human lncRNAs, diseases, Protein Databank, drugs, and drug side effects, are integrated to mine all scientific evidence related to COVID-19. We have employed an automated literature mining and labeling system through a novel approach to measure the…
Purdue University（普渡大学）；University of Pennsylvania（宾夕法尼亚大学）
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predic…
As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach. Relevant Tweets are retrieved from Twitter's API and processed through tokenization and vectorization. After that, these processed vectors are passe…
University of Rome Tor Vergata（罗马第二大学）；Dpt. of Civil Engineering and Computer Science（土木工程与计算机科学系）
Four packages in R are analyzed to carry out sentiment analysis. All packages allow to define custom dictionaries. Just one - Sentiment R - properly accounts for the presence of negators.
Courant Institute（库朗研究所）；Center for Data Science（数据科学中心）；New York University（纽约大学）
This paper describes a builder entry, named "strawman", to the sentence-level sentiment analysis task of the "Build It, Break It" shared task of the First Workshop on Building Linguistically Generalizable NLP Systems. The goal of a builder is to provide an automated sentiment analyzer that would serve as a target for breakers whose goal is to find pairs of minimally-differing sentences that break the analyzer.
