Detecting and analyzing topics of massive COVID-19 related tweets for various countries.
Comput Electr Eng
; 106: 108561, 2023 Mar.
Article
in English
| MEDLINE | ID: covidwho-2165199
ABSTRACT
With the flare-up of the COVID-19 infection since 2020, COVID-19 has been one of the hottest topics on Twitter. Topic modeling is one of the most popular analyses, which extracts the topics from the text. This paper proposes a method to extract the most-discussed topics for 32 countries of the world. In this regard, more than five million related tweets have been studied, and a method based on content analysis is proposed to identify the exact location of each tweet. Then, by using the statistical algorithm of Latent Dirichlet Allocation, the main topics of the tweets are identified. By leveraging sentiment analysis, the topics are afterward divided into positive and negative groups, and their trends in a quarterly period are investigated for the countries under study. The outcome of the analysis of time trends shows that for most countries, the trend of negative topics is highly correlated with the number of confirmed cases of COVID-19.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
/
Qualitative research
/
Randomized controlled trials
Language:
English
Journal:
Comput Electr Eng
Year:
2023
Document Type:
Article
Affiliation country:
J.compeleceng.2022.108561
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