Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline

Karagkiozidou, Makrina; Koukaras, Paraskevas; Tjortjis, Christos

doi:10.1007/978-3-031-08337-2_29

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…According to them, opinion mining, emotion analysis, and/or subjectivity analysis are defined as: "the areas that deal with the computational processing of opinion, emotion, and subjectivity in the text". SA is utilized for extracting knowledge [4][5][6][7][8][9][10] from the Internet and SMs, which have been established as multi-functional networking tools [11], enabling various mining tasks in complex information networks [12]. Today, more than ever, we need ways and approaches to manage massive amounts of data and mitigate global disasters like COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

Koukaras,

Rousidis,

Tjortjis

2023

Informatics

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The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluate public attitudes towards the spread of COVID-19 by performing sentiment analysis on over 2.1 million tweets in English. The implications included the generation of insights for timely disease outbreak prediction and assertions regarding worldwide events, which can help policymakers take suitable actions. We investigated whether there was a correlation between public sentiment and the number of cases and deaths attributed to COVID-19. The research design integrated text preprocessing (regular expression operations, (de)tokenization, stopwords), sentiment polarization analysis via TextBlob, hypothesis formulation (null hypothesis testing), and statistical analysis (Pearson coefficient and p-value) to produce the results. The key findings highlight a correlation between sentiment polarity and deaths, starting at 41 days before and expanding up to 3 days after counting. Twitter users reacted to increased numbers of COVID-19-related deaths after four days by posting tweets with fading sentiment polarization. We also detected a strong correlation between COVID-19 Twitter conversation polarity and reported cases and a weak correlation between polarity and reported deaths.

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