Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AM3P4TCCB" target="_blank" >RIV/00216208:11320/22:M3P4TCCB - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s40745-021-00358-5" target="_blank" >https://doi.org/10.1007/s40745-021-00358-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s40745-021-00358-5" target="_blank" >10.1007/s40745-021-00358-5</a>
Alternative languages
Result language
angličtina
Original language name
Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response
Original language description
In December 2019, a new pandemic called the coronavirus began ravaging the world. By May 2020, the pandemic had caused great loss of lives and disrupted the way of lives in more ways than one. The nature of the disease saw several strategies to curb its spread rolled out. These strategies included closing of businesses and borders, restriction of movements and working from home, mask mandate among others. With these measures and the effects, many individuals have taken to the social media to express their frustrations, opinions and how the pandemic is affecting them. This study employs dictionary based method for sentiment polarization from tweets related to coronavirus posted on Twitter. We also examine the co-occurrence of words to gain insights on the aspects affecting the masses. The results showed that mental health issues, lack of supplies were some of the direct effects of the pandemic. It was also clear that the COVID-19 prevention guidelines were well understood by those who tweeted. The results from this study may help governments combat the consequences of COVID-19 like mental health issues, lack of supplies e.g. food and also gauge the effectiveness or the reach of their guidelines.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Annals of Data Science
ISSN
2198-5812
e-ISSN
2210-4968
Volume of the periodical
9
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
175-186
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85123161249