  COVID-19 has created a major public health problem worldwide and other
problems such as economic crisis, unemployment, mental distress, etc. The
pandemic is deadly in the world and involves many people not only with
infection but also with problems, stress, wonder, fear, resentment, and hatred.
Twitter is a highly influential social media platform and a significant source
of health-related information, news, opinion and public sentiment where
information is shared by both citizens and government sources. Therefore an
effective analysis of COVID-19 tweets is essential for policymakers to make
wise decisions. However, it is challenging to identify interesting and useful
content from major streams of text to understand people's feelings about the
important topics of the COVID-19 tweets. In this paper, we propose a new
\textit{framework} for analyzing topic-based sentiments by extracting key
topics with significant labels and classifying positive, negative, or neutral
tweets on each topic to quickly find common topics of public opinion and
COVID-19-related attitudes. While building our model, we take into account
hybridization of BiLSTM and GRU structures for sentiment analysis to achieve
our goal. The experimental results show that our topic identification method
extracts better topic labels and the sentiment analysis approach using our
proposed hybrid deep learning model achieves the highest accuracy compared to
traditional models.
