  Because of the current COVID-19 pandemic with its increasing fears among
people, it has triggered several health complications such as depression and
anxiety. Such complications have not only affected the developed countries but
also developing countries such as Nepal. These complications can be understood
from peoples' tweets/comments posted online after their proper analysis and
sentiment classification. Nevertheless, owing to the limited number of
tokens/words in each tweet, it is always crucial to capture multiple
information associated with them for their better understanding. In this study,
we, first, represent each tweet by combining both syntactic and semantic
information, called hybrid features. The syntactic information is generated
from the bag of words method, whereas the semantic information is generated
from the combination of the fastText-based (ft) and domain-specific (ds)
methods. Second, we design a novel multi-channel convolutional neural network
(MCNN), which ensembles the multiple CNNs, to capture multi-scale information
for better classification. Last, we evaluate the efficacy of both the proposed
feature extraction method and the MCNN model classifying tweets into three
sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset,
which is the only public COVID-19 tweets dataset in Nepali language. The
evaluation results show that the proposed hybrid features outperform individual
feature extraction methods with the highest classification accuracy of 69.7%
and the MCNN model outperforms the existing methods with the highest
classification accuracy of 71.3% during classification.
