## 2 Literature Review  
Sentiment analysis techniques can be classified into two categories: _lexicon-based_ and _machine learning_ techniques [11]. The lexicon-based approaches detect the emotion from the semantic polarity of words or phrases in the text [12]. These approaches depend on a predefined list of labeled instances of words or phrases to train their supervised classifiers [13]. Each instance in this lexicon will be associated with one or more emotions. For example, words such as \"great\" and \"wonderful\" are words with positive polarity, whereas \"bad\" and \"scary\" words induce negative feeling. The sentiment polarity score of the text will be computed based on this lexicon [14]. This lexicon can be generated manually or automatically using a few words as a seed to expand the lexicon lists [3]. In [15; 16; 17], the authors utilized pre-definedlexicons, such as VADER [18], to assess the polarity score of a given text in accordance with their positive and negative values. However, creating a lexicon is a time consuming task that requires preparing large entries covering all sentiment words of a certain language. Machine learning approaches depend on extracting features from labeled text and map them to the sentiment polarity of that text. These approaches can be classified into classical machine learning approaches and deep learning approaches. Classical machine learning classifiers, such as support vectors machine (SVM), Logistic Regression, and Naive Bayes, predict the text's sentiment by learning from predefined features to capture different aspects of a given text. [PERSON] et al. [19] used fuzzy inference with VADER sentiment lexicon to label the data into three classes: positive, negative, and neutral. They employed hyper-parametric machine learning classifiers (Naive Bayes, AdaBoost, and Logistic Regression) to analyze 226,668 English tweets related to COVID-19. The F1-score of their proposed model yields up to 79%. Similarly, [PERSON] et al. [20] and [PERSON] et al. [21] used classical machine learning algorithms supported by necessary textual data visualizations to provide insights into COVID-19 sentiment progression. [PERSON] et al. [20] proposed a coarse-grained sentiment analysis for COVID-19 tweets using Naive Bayes and Logistic Regression. These machine learning classifiers obtained accuracies of 91% and 75%, respectively, on a dataset consisting of 140 English labeled tweets. Naive Bayes classifier is also used by [PERSON] et al. [21] with VADER lexicon for COVID-19 sentiment analysis. Another interesting work within this category by [PERSON] et al. [22] investigated the COVID-19 vaccination awareness among the public via sentiment analysis and predictive modeling (i.e., Machine Learning). Unsupervised lexicon-based approaches were applied to data sets that contain 1.2 million tweets, by using the publicly available tools TextBlob and VADER, to get the sentiment of each tweet. Various classification algorithms (e.g., SVM, Random Forest, and Linear Regression) were applied to build a forecasting model classifier. Recently, deep learning techniques have achieved a significant success in many domains, including sentiment analysis. Deep learning offers several ways of learning the text representation in supervised and unsupervised ways with the help of the hierarchy of model layers [23]. As can be observed from several review studies [24; 25; 26; 27], the most popular deep learning models used for sentiment analysis are Convolution Neural Network (CNN) [28], Deep Belief Networks [29], Recurrent Neural Network (which includes both GRU and LSTM) [30; 31; 32], Bi-directional Recurrent Neural Network [33], and Attention-based networks [9; 34]. [PERSON] et al. [35] utilized simple deep learning model (DNN) and LSTM, with different word embeddings for sentiment analysis of COVID-19 tweets.