## 2 Literature Review
The proposed models were trained on two English datasets (i.e., Sentiment140 [36] and Emotional-Tweet [37]) to classify the COVID-19 related tweets into negative (disgust, anger, fear, sad) or positive (joy, surprise) sentiments. [PERSON] et al. [38] used CNN with LSTM for classifying the text into three classes: Analytical, Depressed, and Angry. The proposed technique was evaluated on a dataset consisting of 1120 Facebook comments related to COVID-19 in Bangla language and a F1-score of 0.72 was reported. [PERSON] et al. [39] analyzed Chinese Weibo posts by fine-tuning the BERT transformer to classify the sentiment of COVID-19 related posts. A dataset consisting of 120,000 Chinese Weibo posts was used to train and evaluate this model and a F1-score of 0.75 was reported. Another study was performed by [PERSON] and [PERSON] [40] to analyze restaurants' reviews posted on Yelp.com to help restaurants better understand customers' needs during the COVID-19 pandemic. The authors showed that deep learning algorithms, bidirectional LSTM, and simple Embedding with Average Pooling, outperformed classical machine learning algorithms in a sentiment prediction on a dataset consisting of 112,412 restaurant reviews. In a recent study by [PERSON] and [PERSON] [41], the authors developed two deep learning models for fine-grained sentiment analysis that were applied to a unique emotion dataset using COVID-19 tweets for categorizing 10 different emotion labels. The first model consisted of a custom Q&A RoBERTa head to extract the key phrase, which is primarily responsible for the corresponding emotion of a tweet. The second model is proposed for emotion classification employing BiLSTM with attention layer and auxiliary features input. Their study shows how negative emotions evolved throughout the pandemic and how they grew more optimistic over time. Few approaches have been proposed for sentiment analysis of Arabic COVID-19 tweets [9; 42; 43]. Two of these approaches employed classical machine learning algorithms to classify the tweets into positive, negative, and neutral sentiments [42; 43]. [PERSON] et al. [42] trained their model on 10,623 tweets labeled manually with positive, negative, and neutral sentiments and an F1-score of 0.84 was reported using SVM with Bigram term frequency-inverse document frequency (TF-IDF). [PERSON] et al. [43] built an Arabic sentiment lexicon to assign a sentiment value for each tweet in a dataset consisting of 129,391 Arabic tweets. Then, a SVM classifier is applied to report an accuracy and F1-score of 0.98 and 0.98, respectively. [PERSON] et al. [9] proposed a multi-label emotion classifier for Arabic COVID-19 tweets based on AraBERT model [44]. The proposed model is a BERT-based transformer model trained on the Arabic corpus. The authors reported an F1-Macro score of 0.52 on the SenWave dataset, which has been proposed in this work. The proposed dataset consists of 10,000 Arabic tweets related to COVID-19 and it is annotated with 11 emotion classes. Another multi-label emotion detection system in COVID-19 context is proposed by [PERSON] et al. [45]. The proposed system is used to study the evolution of emotions from India-specific tweets towards the COVID-19 pandemic. Two attention-based transformers, RoBERTa and BERT, have been trained on the English version of SenWave dataset and a F1-Macro score of 0.554 was reported. A summary of the surveyed sentiment analysis systems in COVID-19 context is shown in Table 1. As shown in the table, the progress is still slow towards building sentiment analysis systems for Arabic language. This can be attributed to several challenges such as the morphological complexity of Arabic language, different dialects, and data availability. These challenges limit the application of deep learning techniques for Arabic sentiment analysis, specifically at the finer level.