Cited by 23Sentiment Analysis and Opinion MiningCommunication and COVID-19 ImpactMental Health via Writing

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak

Open access
Mittal, Ruchi, Mittal, Amit, Aggarwal, Ishan·2021·Social Network Analysis and Mining

Abstract

This study aims to conduct text mining of affective valence of the sentiments generated on social media during the COVID-19 and measure their association with different outcomes of the disease. 50,000 tweets per day over 23 days during the pandemic were extracted using the VADER sentiment analysis tool. Overall, tweets could effectively be classified in terms of polarity, i.e., "positive," "negative" and "neutral" sentiments. Furthermore, on a day-to-day basis, the study identified a positive and significant relationship between COVID-19-related (a) global infections and negative tweets, (b) global deaths and negative tweets, (c) recoveries and negative tweets, and (d) recoveries and positive tweets. No significant association could be found between (e) infections and positive tweets and (f) deaths and positive tweets. Furthermore, the statistical analysis also indicated that the daily distribution of tweets based on polarity generates three distinct and significantly different numbers of tweets per category, i.e., positive, negative and neutral. As per the results generated through sentiment analysis of tweets in this study, the emergence of "positive" tweets in such a gloomy pandemic scenario shows the inherent resilience of humans. The significant association between news of COVID-19 recoveries and positive tweets seems to hint at a more optimistic scenario whenever the pandemic finally comes to an end or is controlled. Such public reactions-for good-have the potential to go viral and influence several others, especially those who are classified as "neutral" or fence-sitters.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s13278-021-00828-x

Cites

93 of 93 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

1 of the 94 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Mittal, R., Mittal, A., & Aggarwal, I.
Affiliations
Chitkara University (IN)
Corresponding author
Mittal, Amit
Venue
Social Network Analysis and Mining
Published
2021-10-27
Volume
11
Issue
1
Pages
108-108
ISSN
  • 1869-5450 (ISSN-L)
  • 1869-5469
OpenAlex
W3208189837
Cited by
23
References
94
Citations by year
2026: 2 · 2025: 2 · 2024: 3 · 2023: 12 · 2022: 4
FWCI
1.67
Type
journal article
Language
en
Open access
Locations
  • Social Network Analysis and Mining (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Communication › Communication and COVID-19 Impact
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
COVID-19 pandemic, Twitter, sentiment analysis, emotional valence, social media, public perception, VADER, COVID-19 mortality, recovery, human resilience

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review