## 2 Related Work  
For computer scientists and researchers, social media data are valuable assets for understanding people's sentiments regarding current events, especially those related to events with worldwide impacts, such as the COVID-19 pandemic. Therefore, the classification of these sentiments yields remarkable findings. For example, in one of the earliest related publications, [PERSON] and colleagues [34] classified (negative, positive, and neutral) tweets based on word-level, bi-gram, and tri-gram frequencies to represent word rates by power law distribution and applied the Python built-in package TextBlob to perform sentiment analysis. [PERSON] and colleagues [35] proposed machine learning models (naive Bayes and logistic regression) to categorize sentiment tweets into two classes (positive and negative). Similarly, [PERSON] et al. [36] analyzed a large Arabic COVID-19-related tweets dataset, applying uni-gram and bi-gram TF-IDF with SVM, naive Bayes, and KNN classifiers to enhance accuracy. [PERSON] et al. [37] classified the tweets into three classes (positive, neutral, and negative). They utilized different classifiers, such as random forest, SVM, decision tree, naive Bayes, LogitBoost, and MaxEntropy. More recently, [PERSON] and colleagues [38] classified positive, negative, and neutral tweets using various feature sets and XGBoost (eXtreme Gradient Boosting) classifier. The authors of [PERSON] et al. [39] proposed a COVID-19 tweets classification approach based on a decision tree, XGBoost, extra tree classifier (ETC), random forest, and LSTM. Similarly, [PERSON] et al. [40] proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD) based on five different machine learning models (logistic regression, random forest classifier, multinomial NB classifier, support vector machine, and decision tree classifier)  
[PERSON] et al. [41] explored the performance of ensemble machine learning classifiers for sentiment analysis of COVID-19 tweets from the United Kingdom. [PERSON] et al. [42] applied MapReduce opinion mining for COVID-19-related tweets classification using an enhanced ID3 decision tree classifier. [PERSON] et al. [43] presented a model that combines five models such as naive Bayes support vector machines (NBSVM), FastText, DistilBERT, CNN, and bidirectional gated recurrent unit (BiGRU) on COVID-19 tweets in eight highly affected countries. [PERSON] et al. [44] proposed a hierarchical Twitter sentiment model (HTSM) to show people's opinions in short texts. [PERSON] et al. [45] proposed a novel approach for investigating the COVID-19 discussions on Twitter through a multilayer network-based model. It yielded the identification of influential users, which is much more important to analyze and can provide more valuable information. [PERSON] et al. [46] correspondingly proposed the use of various pre-trained embedding representations--FastText, GloVe, Word2 Vec, and BERT--to extract features from a Twitter dataset. Furthermore, for the classification, they applied deep learning methods Bi-LSTM and several classical machine learning classifiers, such as SVM and naive Bayes. [PERSON] et al. [47] reported public sentiment toward COVID-19 vaccines across Canadian cities by analyzing comments on Reddit. In order to identify significant latent topics and classify sentiments in COVID-19-related English comments between January and March 2020, [PERSON] et al., examined 563,079 comments from Reddit [48]. [PERSON] et al. [49] analyzed 522 comments from a Reddit Ask Me Anything session about COVID-19. Reddit posts evaluated in this study were manually coded by two authors of this paper. [PERSON] et al. [50] showed that new knowledge could be captured and tracked using the temporal change in word embeddings from the abstracts of COVID-19 published articles. They found that thromboembolic complications were detected as an emerging theme as of August 2020.