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.

翻译：COVID-19疫情已在全球夺走数百万生命，并引发了强烈的情感反应。本研究分析了美国与印度两国在超过5400万条推文中表达的与COVID-19相关的情感，时间跨度为2020年2月至2021年4月（共15个月），其中包括2021年3月起席卷印度的灾难性病例激增初期。利用预训练情感分析与主题建模算法，我们研究了四种特定情感类型（恐惧、愤怒、快乐与悲伤）及其随时间和地域变化的特征。结果显示，两国在恐惧、愤怒和快乐相对比例上存在显著差异，且这些比例随时间变化：恐惧情绪持续下降，而愤怒与快乐在2020年期间波动起伏，直至2021年前四个月的新形势逆转了这一趋势。本文通过揭示潜在主题，并结合情感评价理论框架，简要讨论了所检测到的差异及其研究意义。