Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak
Open accessAbstract
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.
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- 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
- PMC
- PMC8548272
- 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
- bronze
- doi.org
- 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 ›
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