## 2 Related Works
### Arabic Text Classification (ATC)  
Arabic text classification is the most important phase for categorizing the different contextual Arabic contents into a proper category. Many machine learning algorithms have been proposed for Arabic text categorization, for example. In [34], [PERSON] et al. presented a Naive Bayes classifier to classify Arabic text documents into five classes. They used TF-IDF to represent Arabic text. [PERSON] et al. proposed a combination method of term frequency-inverse document frequency (TF-IDF) using the capability of CNN to classify Arabic text from a large dataset [39]. In [44], [PERSON] et al. presented a simple hybrid model for CNN and LSTM to classify English text based on character-level inputs. However, output predictions are still made based on the word level. Their evaluation results are reported using six different datasets and presented competitive and promising achieve ments. In [45], [PERSON] et al. addressed the problem of question ranking by addressing the task with machine learning algorithms for Arabic text representations. They designed an LSTM classifier to identify the most proper text segmentations in questions in a binary classification problem. This is to select the meaningful text representation and to reduce the noise as well as the computational cost. Then, they proposed a hybrid model using tree kernels built on the top of constituency parse trees. This model was first built by [PERSON] for Arabic word embedding based on supervised neural networks. In [46], [PERSON] et al. constructed Word2 Vec models from a large Arabic sentiment corpus that collected over ten Arab countries. They applied different ML algorithms and convolutional neural networks with feature selection algorithms to reduce the derived feature dimensionality. The achieved accuracy was between 91.0% and 95.0% for Arabic sentiment classification using a health sentiment dataset. In [47], [PERSON] et al. used the fuzzy C-means (FCM) classifier to enhance the performance of Arabic document classification. For feature dimensionality reduction, they used singular value decomposition (SVD). Due to the problem of Arabic root words, in which one word has many possible meanings and is subject to mistakes, they used FCM to solve this issue. They achieved overall performance in terms of precision, recall, and F-measure by 60.16%, 62.66%, and 61.18%, respectively. In 2020, [PERSON] et al. used the Arabic text representation based on the word level and investigated the bidirectional LSTM network (BiLSTM) to enhance the Arabic sentiment analysis [48]. They applied the forward-backward technique to encapsulate contextual information from Arabic feature sequences. They achieved the overall accuracy in terms of F1-measure by 79.41% using the LABR dataset. [PERSON] et al. (2020) proposed an Arabic text categorization method using Arabic WordNet based on the bag-of-concepts and deep Auto-encoder to represent the Arabic text by eliminating the explicit knowledge that contains semantic vocabularies [49]. In 2020, [PERSON] et al. proposed a hybrid model of CNN and RNN to extract the local features using CNN and then classify the Arabic sentiment via RNN [50]. In [51], [PERSON] et al. proposed the first deep learning character-level ConvNet for English text classification. They used two ConvNets with six convolutional layers with kernel sizes of 3 and 7, three simple max-pooling layers, and three fully connected layers. To verify their model, they used eight large-scale datasets, and they achieved the lowest testing errors for all datasets compared with the traditional methods of bag-of-means, n-grams TF-IDF, LSTM, word2 vec, and lookup table ConvNet. In [18], [PERSON] et al. proposed a deep learning character-level architecture called a very deep CNN (VDCNN) for English text classification. They investigated the depth effectiveness of their deep learning model. They used four different structure depths via 9, 17, 29, and 49 convolutional layers with 4 max-pooling layers for all different structures.