## 2 Related Works
### Arabic Text Representation (ATR)  
In fact, Arabic texts comprise unstructured data in the same way as English texts, and to be understandable for machine learning algorithms, the text must be transformed and represented by numerical values. The text can be represented in different ways using the bag-of-words (BOW) assumption or n-grams and term frequency-inverse document frequency (TF-IDF) [33]. Although these methods have shown good textual classification results, they lose the order of Arabic words, and they have limitations in capturing semantic meaning [34; 35; 36]. [PERSON] et al. proposed a new representation approach for Arabic text called Term Class Weight-Inverse Class Frequency (TCW-ICF). Their representation is used to extract the most promising features from Arabic texts [37]. In [36], [PERSON] et al. presented a graph-based semantic representation model for Arabic text classification. Their proposed model achieved improvements of 8.60%, 30.20%, 5.30%, and 16.20% in terms of overall accuracy, sensitivity, precision, and F1-score, respectively. In contrast, other embedding techniques of Word2 Vec and Glo2 Vec have recently been used to represent conceptual text using deep learning approaches at the word level. Such representation is good for English text because several flexible preprocessing algorithms are available to improve the text representations compared with the structure of Arabic text [16]. In 2018, [PERSON] et al. constructed Word2 Vec models from a large Arabic corpus. They have worked with different ML algorithms and convolutional neural networks (CNN) for Arabic text classification. Although they used the feature selections technique to reduce the input feature dimensionality for better classification performance, some limitations \(\backslash\) still exist for pre-processing, removing numbers, and normalization and did not handle negation of words [38]. In 2018, [PERSON] et al. improved Arabic microblog retrieval with distributed representations for Arabic text classification [39]. In 2020, [PERSON] et al. proposed a method that combined document (doc2 vec) embedding representation and sense disambiguation to enhance Arabic text representation. Then, they conducted experiments using the OSAC corpus dataset. They achieved an overall text classification accuracy of 90% in terms of F-measure [40]. In 2014, [PERSON] [41] proposed the first deep learning model consisting of a shallower convolutional neural network with only one convolutional layer based on the word level. Indeed, the Arabic language has many challenges for Arabic text representation [16]. The aim of this study is to represent Arabic text at the character level instead of at the word level. This is due to the scarcity of Arabic text preprocessing algorithms and to avoid the difficulties and limitations mentioned above. Arabic character-level representation is useful for deep learning ConvNet (i.e., CNN) architectures since they do not suffer from such limitations and have recently shown promising classification results for various NLP applications [42]. Moreover, ConvNet could be directly applied to distributed or discrete word embedding without pre-knowledge on the syntactic or semantic structures of that word. This is the key for developing a single reliable computer-aided recognition (CAR) system for different languages. Language characters always constitute a necessary textual construct regardless of the ability of word segmentation. Thus, working at the character level has the advantage of the ability to naturally learn abnormal character combinations of misspellings and emoticons. In 2020, [PERSON] et al. presented that deep CNN was used for Arabic sentiment analysis text (SA). They represented Arabic text for sentiment analysis using character level features. This work still has some limitations such as that not all characters and numbers in Arabic texts are used, which will create misunderstanding for Arabic text [43].