Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Public discourse and sentiment during the COVID-19 pandemic: using
  Latent Dirichlet Allocation for topic modeling on Twitter   The study aims to understand Twitter users' discourse and psychological
reactions to COVID-19. We use machine learning techniques to analyze about 1.9
million Tweets (written in English) related to coronavirus collected from
January 23 to March 7, 2020. A total of salient 11 topics are identified and
then categorized into ten themes, including "updates about confirmed cases,"
"COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak
in South Korea," "early signs of the outbreak in New York," "Diamond Princess
cruise," "economic impact," "Preventive measures," "authorities," and "supply
chain." Results do not reveal treatments and symptoms related messages as
prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown
nature of the coronavirus is dominant in all topics. Implications and
limitations of the study are also discussed.

28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph