Detecting COVID-19 Pandemic Using Sentiment Analysis of Tweets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A90101%2F21%3A10441826" target="_blank" >RIV/00216208:90101/21:10441826 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=9VIebPvRcs" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=9VIebPvRcs</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Detecting COVID-19 Pandemic Using Sentiment Analysis of Tweets
Original language description
From 2019, the world is facing an unforeseen challenge in the form of COVID-19, which started in Wuhan (China), and within two months, it spread to 212 countries. The coronavirus disease (COVID-19) pandemic puts unprecedented pressure on healthcare systems worldwide. Due to its rapid widespread around the globe affecting the lives of millions, extensive measures to reduce and prevent its transmission have been implemented. One of which is to shut down their cities completely. During this Pandemic, people started to express their situations through social media tools. In natural language processing, valuable insights can be captured from textual data taken from different social media platforms. In this research work, data related to COVID-19 is collected from a popular social networking site, Twitter. The tweets gathered are refined through pre-processing for text mining and sentiment analysis. From this data, we successfully detect the actual count of people who may be affected by the COVID-19 Pandemic using sentimental analysis and machine learning techniques.
Czech name
—
Czech description
—
Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence Theory and Applications
ISSN
2757-9778
e-ISSN
2757-9778
Volume of the periodical
1
Issue of the periodical within the volume
2
Country of publishing house
TR - TURKEY
Number of pages
9
Pages from-to
39-47
UT code for WoS article
—
EID of the result in the Scopus database
—