  The COVID-19 pandemic has claimed millions of lives worldwide and elicited
heightened emotions. This study examines the expression of various emotions
pertaining to COVID-19 in the United States and India as manifested in over 54
million tweets, covering the fifteen-month period from February 2020 through
April 2021, a period which includes the beginnings of the huge and disastrous
increase in COVID-19 cases that started to ravage India in March 2021.
Employing pre-trained emotion analysis and topic modeling algorithms, four
distinct types of emotions (fear, anger, happiness, and sadness) and their
time- and location-associated variations were examined. Results revealed
significant country differences and temporal changes in the relative
proportions of fear, anger, and happiness, with fear declining and anger and
happiness fluctuating in 2020 until new situations over the first four months
of 2021 reversed the trends. Detected differences are discussed briefly in
terms of the latent topics revealed and through the lens of appraisal theories
of emotions, and the implications of the findings are discussed.
