Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining the Relationship Between COVID-19 Sentiment and Market
  Performance   At the beginning of the COVID-19 outbreak in March, we observed one of the
largest stock market crashes in history. Within the months following this, a
volatile bullish climb back to pre-pandemic performances and higher. In this
paper, we study the stock market behavior during the initial few months of the
COVID-19 pandemic in relation to COVID-19 sentiment. Using text sentiment
analysis of Twitter data, we look at tweets that contain key words in relation
to the COVID-19 pandemic and the sentiment of the tweet to understand whether
sentiment can be used as an indicator for stock market performance. There has
been previous research done on applying natural language processing and text
sentiment analysis to understand the stock market performance, given how
prevalent the impact of COVID-19 is to the economy, we want to further the
application of these techniques to understand the relationship that COVID-19
has with stock market performance. Our findings show that there is a strong
relationship to COVID-19 sentiment derived from tweets that could be used to
predict stock market performance in the future.

3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph