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Since its outbreak, the ongoing COVID-19 pandemic has caused unprecedentedlosses to human lives and economies around the world. As of 18th July 2020, theWorld Health Organization (WHO) has reported more than 13 million confirmedcases including close to 600,000 deaths across 216 countries and territories.Despite several government measures, India has gradually moved up the ranks tobecome the third worst-hit nation by the pandemic after the US and Brazil, thuscausing widespread anxiety and fear among her citizens. As majority of theworld's population continues to remain confined to their homes, more and morepeople have started relying on social media platforms such as Twitter forexpressing their feelings and attitudes towards various aspects of thepandemic. With rising concerns of mental well-being, it becomes imperative toanalyze the dynamics of public affect in order to anticipate any potentialthreats and take precautionary measures. Since affective states of human mindare more nuanced than meager binary sentiments, here we propose a deeplearning-based system to identify people's emotions from their tweets. Weachieve competitive results on two benchmark datasets for multi-label emotionclassification. We then use our system to analyze the evolution of emotionalresponses among Indians as the pandemic continues to spread its wings. We alsostudy the development of salient factors contributing towards the changes inattitudes over time. Finally, we discuss directions to further improve our workand hope that our analysis can aid in better public health monitoring.

How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian Emotions Through The Lens of Twitter