  How different cultures react and respond given a crisis is predominant in a
society's norms and political will to combat the situation. Often the decisions
made are necessitated by events, social pressure, or the need of the hour,
which may not represent the will of the nation. While some are pleased with it,
others might show resentment. Coronavirus (COVID-19) brought a mix of similar
emotions from the nations towards the decisions taken by their respective
governments. Social media was bombarded with posts containing both positive and
negative sentiments on the COVID-19, pandemic, lockdown, hashtags past couple
of months. Despite geographically close, many neighboring countries reacted
differently to one another. For instance, Denmark and Sweden, which share many
similarities, stood poles apart on the decision taken by their respective
governments. Yet, their nation's support was mostly unanimous, unlike the South
Asian neighboring countries where people showed a lot of anxiety and
resentment. This study tends to detect and analyze sentiment polarity and
emotions demonstrated during the initial phase of the pandemic and the lockdown
period employing natural language processing (NLP) and deep learning techniques
on Twitter posts. Deep long short-term memory (LSTM) models used for estimating
the sentiment polarity and emotions from extracted tweets have been trained to
achieve state-of-the-art accuracy on the sentiment140 dataset. The use of
emoticons showed a unique and novel way of validating the supervised deep
learning models on tweets extracted from Twitter.
