## 2 Literature Review
[35] compared the Support Vector Machine (SVM) and Artificial Neural Network (ANN) for detecting sentiment on 3000 sentences from the Movie Review dataset and 3000 customer reviews on Amazon for particular products. According to their experiments, ANN performed significantly better than SVM on sentiment classification. Ref. [24] carried out a study on the scalability of the Naive Bayes classifier for big data, and found the Naive Bayes classifier to be capable of analyzing millions of movie reviews with 82% accuracy using a vast dataset. Although Arabic is a less-researched area with respect to sentiment mining, there have been some reports. Ref. [36] compared the performance of five classifiers: Support Vector Machine, Random Gees, Gaussian Naive Bayes, Logistic Regression, and Stochastic Gradient Descent. They also compared the Skip-Gram model and Continuous Bag of Words (CBOW) by fastText as vectorization methods. They found that fastText Skip-Gram performed better for all classifiers. Ref. [37] examined Hierarchical Classifier, SVM, Decision Tree (DT), Naive Bayes, K-Nearest Neighbor (KNN) on an extensive book review dataset. They found that their Hierarchical Classifier achieved 57.8% accuracy. Ref. [38] compared Convolutional Neural network (CNN), Naive Bayes, Logistic Regression and Support Vector Machine (SVM) for sentiment classification on a health dataset. According to their experiments, SVM gave the best results. However, there was no common agreement among researchers regarding high-performance algorithms dealing with sentiment analysis on the basis of those experiments. Some algorithms may work very well under certain conditions, but fail on the problem set. The aforementioned supervised classifier fails to capture word order sequences in the text. Most machine learning algorithms cannot distinguish differences in word order, such as between \"the dog kills a mouse\" and \"the mouse kills a dog\". A machine learning architecture is needed that is aware of time sequences. Recurrent Neural Network (RNN) has connections between previous, current and future signals, and therefore it is fit to represent time-series signals, including text. Ref. [39] proposed an implementation of RNN called LSTM (Long Short-Term Memory) in order to retain the long-term memory effect. Some researchers have implemented RNN and LSTM in many languages, including Arabic, as shown in Table 1. Ref. [9] proposed an Indonesian sentiment corpus and classification engine using word2 vec and LSTM. In Ref. [40], proposed Hybrid CNN-LSTM Model outperformed traditional deep learning and machine learning techniques in precision, recall, f-measure, and accuracy. Hybrid CNN-LSTM was tested on the IMDBsentiment dataset and achieved an improved accuracy of 91%. Ref. [41] explored the performance of a deep learning framework for an Arabic corpus of around 40,000 tweets using Word2 vec and several architectures. According to their experiments, LSTM with data augmentation to balance the dataset overperformed compared to LSTM without data augmentation, and the CNN and RCNN models. Ref. [42] implemented CNN for feature extraction and LSTM to capture long-term word dependency. They achieved 64% accuracy on three-class sentiment prediction. Ref. [41] focused on a health services sentiment dataset. They used the English dataset translated into Arabic, carried out the classification using RCNN, and achieved 94% prediction accuracy. Ref. [30] implemented LSTM on a small corpus with five classes in two Arabic dialects: Emirati and Egyptian. They achieved accuracies of 70% on Egyptian dialects and 63.7% on Emirati dialects. Ref. [43] performed an exercise using multiple datasets, word embedding methods, various classic machine learning methods, and a deep learning framework. They relied on fastText word embedding for deep learning using CNN, LSTM, and bidirectional LSTM. Ref. [21] explored the Recursive Neural Tensor Networks (RNTN) model using the word2 vec word embedding method. Ref. [44] explored CNN and two-layer LSTM.