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:本研究检视了自2020年2月至2021年4月的15个月期间,覆盖超过5400万条推特,分析美国和印度民众关于COVID-19的不同情绪表达。这段时间包括了2021年3月开始肆虐的印度 COVID-19 病例激增情况。采用预训练情感分析和主题建模算法,研究了四种不同类型的情绪(恐惧、愤怒、快乐和悲伤)及其与时间和地点相关的变化。研究结果显示了显著的国家差异和与时间变化相关的相对情绪比例的差异,其中恐惧情绪下降,愤怒和快乐情绪波动不定,直至2021年前四个月新情况出现改变了这种趋势。通过情绪评估理论的思考和研究所暴露的潜在主题,简要讨论了不同之处,并讨论了发现的含义。