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.