  Social scientists and psychologists take interest in understanding how people
express emotions or sentiments when dealing with catastrophic events such as
natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a
catastrophic event that has raised a number of psychological issues such as
depression given abrupt social changes and lack of employment. During the rise
of COVID-19 cases with stricter lock downs, people have been expressing their
sentiments in social media which can provide a deep understanding of how people
physiologically react to catastrophic events. In this paper, we use deep
learning based language models via long short-term memory (LSTM) recurrent
neural networks for sentiment analysis on Twitter with a focus of rise of novel
cases in India. We use the LSTM model with a global vector (GloVe) for word
representation in building a language model. We review the sentiments expressed
for selective months covering the major peak of new cases in 2020. We present a
framework that focuses on multi-label sentiment classification using LSTM model
and GloVe embedding, where more than one sentiment can be expressed at once.
Our results show that the majority of the tweets have been positive with high
levels of optimism during the rise of the COVID-19 cases in India. We find that
the number of tweets significantly lowered towards the peak of new cases. We
find that the optimistic and joking tweets mostly dominated the monthly tweets
and there was a much lower number of negative sentiments expressed. This could
imply that the majority were generally positive and some annoyed towards the
way the pandemic was handled by the authorities as their peak was reached.
