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Because of the current COVID-19 pandemic with its increasing fears amongpeople, it has triggered several health complications such as depression andanxiety. Such complications have not only affected the developed countries butalso developing countries such as Nepal. These complications can be understoodfrom peoples' tweets/comments posted online after their proper analysis andsentiment classification. Nevertheless, owing to the limited number oftokens/words in each tweet, it is always crucial to capture multipleinformation associated with them for their better understanding. In this study,we, first, represent each tweet by combining both syntactic and semanticinformation, called hybrid features. The syntactic information is generatedfrom the bag of words method, whereas the semantic information is generatedfrom 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 informationfor better classification. Last, we evaluate the efficacy of both the proposedfeature extraction method and the MCNN model classifying tweets into threesentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset,which is the only public COVID-19 tweets dataset in Nepali language. Theevaluation results show that the proposed hybrid features outperform individualfeature extraction methods with the highest classification accuracy of 69.7%and the MCNN model outperforms the existing methods with the highestclassification accuracy of 71.3% during classification.

Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features