COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes Article Swipe
Raj Kumar Gupta
,
Ajay Vishwanath
,
Yinping Yang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.06954
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.06954
This paper describes a large global dataset on people's discourse and responses to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 June 2022, we collected and processed over 252 million Twitter posts from more than 29 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging probabilistic topic modelling and pre-trained machine learning-based emotion recognition algorithms, we labelled each tweet with seventeen attributes, including a) ten binary attributes indicating the tweet's relevance (1) or irrelevance (0) to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: extremely negative to 1: extremely positive) and the degree of intensity of fear, anger, sadness and happiness emotions (from 0: not at all to 1: extremely intense), and c) two categorical attributes indicating the sentiment (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion (fear, anger, sadness, happiness, no specific emotion) the tweet is mainly expressing. We discuss the technical validity and report the descriptive statistics of these attributes, their temporal distribution, and geographic representation. The paper concludes with a discussion of the dataset's usage in communication, psychology, public health, economics, and epidemiology.
Related Topics
Concepts
Sadness
Happiness
Anger
Sentiment analysis
Valence (chemistry)
Psychology
Categorical variable
Social media
Emotion classification
Expressed emotion
Coronavirus disease 2019 (COVID-19)
Computer science
Cognitive psychology
Social psychology
Artificial intelligence
Machine learning
World Wide Web
Developmental psychology
Medicine
Disease
Quantum mechanics
Physics
Infectious disease (medical specialty)
Pathology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.06954
- https://arxiv.org/pdf/2007.06954
- OA Status
- green
- Cited By
- 43
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3042875976
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3042875976Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2007.06954Digital Object Identifier
- Title
-
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions AttributesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-14Full publication date if available
- Authors
-
Raj Kumar Gupta, Ajay Vishwanath, Yinping YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.06954Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.06954Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2007.06954Direct OA link when available
- Concepts
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Sadness, Happiness, Anger, Sentiment analysis, Valence (chemistry), Psychology, Categorical variable, Social media, Emotion classification, Expressed emotion, Coronavirus disease 2019 (COVID-19), Computer science, Cognitive psychology, Social psychology, Artificial intelligence, Machine learning, World Wide Web, Developmental psychology, Medicine, Disease, Quantum mechanics, Physics, Infectious disease (medical specialty), PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
43Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 11, 2022: 10, 2021: 11, 2020: 7Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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