  Social scientists and psychologists take interest in understanding how people
express emotions and 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. Advancements of
deep learning-based language models have been promising for sentiment analysis
with data from social networks such as Twitter. Given the situation with
COVID-19 pandemic, different countries had different peaks where the rise and
fall of new cases affected lock-downs which directly affected the economy and
employment. During the rise of COVID-19 cases with stricter lock-downs, people
have been expressing their sentiments in social media. This can provide a deep
understanding of human psychology during catastrophic events. In this paper, we
present a framework that employs deep learning-based language models via long
short-term memory (LSTM) recurrent neural networks for sentiment analysis
during the rise of novel COVID-19 cases in India. The framework features LSTM
language model with a global vector embedding and state-of-art BERT language
model. We review the sentiments expressed for selective months in 2020 which
covers the first major peak of novel cases in India. Our framework utilises
multi-label sentiment classification where more than one sentiment can be
expressed at once. Our results indicate that the majority of the tweets have
been positive with high levels of optimism during the rise of the novel
COVID-19 cases and the number of tweets significantly lowered towards the peak.
The predictions generally indicate that although the majority have been
optimistic, a significant group of population has been annoyed towards the way
the pandemic was handled by the authorities.
