  While COVID-19 has impacted humans for a long time, people search the web for
pandemic-related information, causing anxiety. From a theoretic perspective,
previous studies have confirmed that the number of COVID-19 cases can cause
negative emotions, but how statistics of different dimensions, such as the
number of imported cases, the number of local cases, and the number of
government-designated lockdown zones, stimulate people's emotions requires
detailed understanding. In order to obtain the views of people on COVID-19,
this paper first proposes a deep learning model which classifies texts related
to the pandemic from text data with place labels. Next, it conducts a sentiment
analysis based on multi-task learning. Finally, it carries out a fixed-effect
panel regression with outputs of the sentiment analysis. The performance of the
algorithm shows a promising result. The empirical study demonstrates while the
number of local cases is positively associated with risk perception, the number
of imported cases is negatively associated with confidence levels, which
explains why citizens tend to ascribe the protracted pandemic to foreign
factors. Besides, this study finds that previous pandemic hits cities recover
slowly from the suffering, while local governments' spending on healthcare can
improve the situation. Our study illustrates the reasons for risk perception
and confidence based on different sources of statistical information due to
cognitive bias. It complements the knowledge related to epidemic information.
It also contributes to a framework that combines sentiment analysis using
advanced deep learning technology with the empirical regression method.
