This paper describes a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users since 28 January to 1 July 2020. As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1) ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively. To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss  possible usage in communication, psychology, public health, economics and epidemiology research.
This paper presents a large annotated dataset on public expressions related to the COVID-19 pandemic. Through Twitter's standard search application programming interface, we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020. Using natural language processing techniques and machine learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes , including: 1) ten binary attributes indicating the tweet's relevance or irrelevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity) , and 3) two qualitative attributes indicating the sentiment category and the dominant emotion category, respectively. We report basic descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss its possible usage in communication, psychology, public health, economics and epidemiology research.
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describes a large dataset covering
presents a large annotated dataset on public expressions related to the COVID-19 pandemic. Through Twitter's standard search application programming interface, we retrieved
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Twitter
public
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As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using
Using
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machine-learning
machine learning
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and inferred
we annotated each public tweet with
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associated with each tweet, including
, including:
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binary
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or irrelevance
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in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear
of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity)
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anger, sadness
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joy, and
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most
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To illustrate how the dataset can be used, we present
We report basic
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its