## 2 Related Work  
DL has recently demonstrated its effectiveness compared to the state-of-the-art performance of standard ML methods for sentiment analysis. To identify sentiments in SemEval 2017, ASTD, and ARSAS, the authors in [17] utilized a CNN and LSTM. They generated a word embedding matrix using word2 vec. Their model achieved the highest performance for SemEval 2017 and ASTD datasets. In [7], for the binary classification of Arabic attitudes, the authors suggested a hybrid CNN-LSTM model with word2 vec embeddings. They used many Arabic sentiment analysis datasets, such as Main-AHS, Ar-Twitter, and ASTD. The hybrid CNN-LSTM model registered the highest accuracy at 79.07%. The authors of [18] applied CNN on nine datasets, including LABR and ASTD, to analyze a binary sentiment analysis. The dataset includes two domains: reviews and tweets. The two types of word2 vec of CBOW and Skip-Gram were used to generate the word embedding matrix. Additionally, they used CNN on both balanced and unbalanced datasets. In [8], the authors proposed a hybrid LSTM-RNN model based on LSTM and RNN to analyze Arabic sentiment. They studied the effect of using different pre-trained word embeddings with DL models. They tested their model with the AraSenTi-Tweet. In [19], the authors introduced an Arabic language dataset about health services from Twitter. They added annotations to tweets and pre-processed them into good and negative tweets. The authors applied NB, SVM, LR, and CNN to the health dataset. Using character-level data, the authors [20] deployed deep CNNs for Arabic sentiment analysis. The proposed large dataset was built on the available Arabic sentiment analysis datasets in different domains (modern standard, dialect) to train networks. In addition, different ML algorithms, such as LR, SVM, and NB, have been applied to assess the performance on a large dataset. The results show that deep CNNs registered the highest accuracy compared to ML classifiers. In [21], the authors investigated the bidirectional LSTM network (BiLSTM) to analyze Arabic sentiment analysis. Six Arabic datasets were used to train and evaluate their proposed model, the DL model and the ML model. Their proposed model achieved the highest performance compared to the performance of DL and ML models. In [22], the authors applied different ML algorithms and CNNs models with other feature extraction methods. They used Main-AHS Sub-AHS heath Arabic datasets. The result showed that CNN improved accuracy from 91 to 95% for publicly available Arabic language health sentiment datasets. Ensemble models could enhance the inference power of single models. In addition, using hybrid models as base classifiers in an ensemble could boost the performance of hybrid models [9]. Ensemble models have been applied in different domains and achieved better results than base models [23]. In the Arabic sentiment analysis domain, ensemble modeling has been applied, such as in [9], where the authors proposed an ensemble model using voting for optimizing the Arabic sentiment analysis. They applied CNN-LSTM and the optimization method to select the best CNN and LSTM on the Arabic sentiment tweets dataset (ASTD). The selected models are the ones achieving the highest f1-score among other models. The authors of [4] proposed an Arabic sentiment dataset about COVID-19-related conspiracy theories. The collected data were annotated into positive and negative class labels. They applied RF, SVM, NB, and LR to the collected dataset and the SMOTE technique to handle unbalanced data. Furthermore, they used the voting classifier combining RF, SGD, SVM, BNB, and LR. The experimental results showed that applying the ensemble model with SMOTE improved the performance. In [22], the authors studied the effect of combining multi models (NB, SVM, and maximum entropy) using the voting algorithm on Arabic sentiment analysis. They applied models to different stemming, such as Khoja, ISRI, Tashiphyne, Light10, and MOTAZ. The outcomes demonstrated that the voting algorithm delivered the best results.