India is a hotspot of the COVID-19 crisis. During the first wave several lockdowns (L) and gradual unlock (UL) phases were implemented by the Government of India (GOI) to curb the virus spread. Twitter, a social media platform, was extensively used by citizens to react to various events and topics related to resource management and virus spread that varied geographically. This paper attempts to capture those variations by analyzing the sentiments of geotagged tweets during L and UL phases, which remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings can aid decision-makers in analyzing citizen reactions toward the resources and events during an ongoing pandemic, which can result in better resource planning.