  Crises such as natural disasters, global pandemics, and social unrest
continuously threaten our world and emotionally affect millions of people
worldwide in distinct ways. Understanding emotions that people express during
large-scale crises helps inform policy makers and first responders about the
emotional states of the population as well as provide emotional support to
those who need such support. We present CovidEmo, ~3K English tweets labeled
with emotions and temporally distributed across 18 months. Our analyses reveal
the emotional toll caused by COVID-19, and changes of the social narrative and
associated emotions over time. Motivated by the time-sensitive nature of crises
and the cost of large-scale annotation efforts, we examine how well large
pre-trained language models generalize across domains and timeline in the task
of perceived emotion prediction in the context of COVID-19. Our analyses
suggest that cross-domain information transfers occur, yet there are still
significant gaps. We propose semi-supervised learning as a way to bridge this
gap, obtaining significantly better performance using unlabeled data from the
target domain.
