  Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus,
which soon spread all over the world, resulting in a deadly pandemic that
infected millions of people around the globe. The government and public health
agencies followed many strategies to counter the fatal virus. However, the
virus severely affected the social and economic lives of the people. In this
paper, we extract and study the opinion of people from the top five worst
affected countries by the virus, namely USA, Brazil, India, Russia, and South
Africa. We propose a deep language-independent Multilevel Attention-based
Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level
encoded attention, and sentence-level encoded attention mechanism to extract
the positive, negative, and neutral sentiments. The embedding layer encodes the
sentence sequence into a real-valued vector. The word-level and sentence-level
encoding is performed by a 1D Conv-BiGRU based mechanism, followed by
word-level and sentence-level attention, respectively. We further develop a
COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive
experiments on our proposed dataset demonstrate the effectiveness of the
proposed MACBiG-Net. Also, attention-weights visualization and in-depth results
analysis shows that the proposed network has effectively captured the
sentiments of the people.
