Characterizing public emotions and sentiments in Covid-19 environment
A case study of India
Bibliographic Data
| ID | 3017260 |
|---|---|
| Authors | Subasish Da (0000-0002-1671-2753, Texas A&M Transportation Institute, Texas A&M University, College Station, Texas, USA), Anandi Dutta (0009-0002-7279-7752, The University of Texas at San Antonio) |
| Year | 2021 |
| Volume | 31 |
| Issue | 1-4 |
| Pages | 154-167 |
| Publication date | 2021-05-19 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Human Behavior in the Social Environment (JOURNAL) |
| Journal identifiers | ISSN: 1091-1359 • E-ISSN: 1540-3556 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10911359.2020.1781015 |
| OpenAlex | W3043095242 |
| Language | EN |
| Citations received | 14 |
| References cited | 28 |
Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019–2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected "COVID-19 in India" related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic
2019-20 coronavirus outbreak · China · Christian ministry · Coronavirus · Coronavirus disease 2019 (COVID-19 · Criminology · Development economics · Disease · Economic growth · Economics · Geography · Infectious disease (medical specialty · Outbreak · Pandemic · Pathology · Political science · Public health · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Socioeconomics · Sociology · Welfare · Medicine · Mental Health via Writing · Misinformation and Its Impacts · Psychology · Sentiment Analysis and Opinion Mining · Demography · Law · Virology
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Spatiotemporal patterns of the Covid-19 control measures impact on industrial production in Wuhan using time-series earth observation data
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Effects of Covid-19 pandemic on working lives and psychological health of orthopedic surgeons in Pakistan
Measuring Emotional Wellbeing and Emotional Contagion Through Sentiments and Emotions Evoked by Social Media for Covid-19
Influencing Factors of Environmental Risk Perception during the Covid-19 Epidemic in China
Emotional response patterns, mental health, and structural vulnerability during the Covid-19 pandemic in Canada
The Influence of Covid-19 on Vocational Hospitality and Tourism Students’ Career Choices
Learning from past pandemic governance
Analysing discourse around Covid-19 in the Australian Twittersphere
Rapid assessment of disaster damage using social media activity
Techniques and applications for sentiment analysis
Processing Social Media Messages in Mass Emergency
Crowdsourcing a Word–emotion Association Lexicon
How organizations framed the 2009 H1N1 pandemic via social and traditional media
Sentiment analysis algorithms and applications
Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during Covid-19 pandemic
Opinion Mining and Sentiment Analysis
Social media analytics for natural disaster management
An interactive web-based dashboard to track Covid-19 in real time
Covid-19 and mental health
When the storm is over
Unpacking the black box
Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective – A case study of Hurricane Harvey
A novel approach to leveraging social media for rapid flood mapping
An argument for basic emotions
| Citations per year | 2.33 |
|---|---|
| Citation span | 2020 - 2026 (7) |
| Highly cited | No |