Detecting and analyzing topics of massive COVID-19 related tweets for various countries. (March 2023)

• Record Type: Journal Article
• Authors: Azizi, Faezeh; Hajiabadi, Hamideh; Vahdat-Nejad, Hamed; Khosravi, Mohammad Hossein
• Is Part Of: Computers & electrical engineering. Volume 106(2023)
• Publication Date: 2023-03
• Subjects: Topic modeling -- Twitter social network -- LDA -- Sentiment analysis -- COVID-19; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108561
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The topics of tweets related to COVID-19 are modeled for the entire world and the 32 most affected countries. Tweets are analyzed for location tagging. Countries' Sentiments are categorized into negative countries and positive countries. The time trend of negative and positive topics are analyzed at the world level, continents and countries. 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 . Graphical abstract: Image, graphical abstract