CAViaR-WS-based HAN: conditional autoregressive value at risk-water sailfish-based hierarchical attention network for emotion classification in COVID-19 text review data

The Corona Virus Disease-2019 (COVID-19) pandemic has made a remarkable impact on economies and societies worldwide. With numerous procedures of social distancing and lockdowns, it becomes essential to know people's emotional responses on a very large scale. Thus, an effective emotion classification approach is developed using the proposed Conditional Autoregressive Value at Risk-Water Sailfish-based Hierarchical Attention Network (CAViaR-WS-based HAN) for classifying the emotions in the COVID-19 text review data. The proposed approach, named CAViaR-WS, is designed by the incorporation of Conditional Autoregressive Value at Risk-Sail Fish (CAViaR-SF) and Water Cycle Algorithm (WCA). Here, the significant features, such as mean, variance, entropy, Term Frequency-Inverse Document Frequency (TF-IDF), SentiWordNet features, and spam word-based features, are extracted to further processing. Based on the extracted features, feature fusion is accomplished using the RideNN. In addition, CAViaR-SF-based GAN is used to perform the spam classification, and then, the emotion classification is carried out using Hierarchal Attention Networks (HAN), where the training procedure of HAN is performed using proposed CAViaR-WS. Furthermore, the developed CAViaR-WS-based HAN offers effective performance results concerning precision, recall, and f-measure with the maximal values of 0.937, 0.958, and 0.948, respectively.