## 3 Materials and Methods
### Dataset
#### 3.1.1 Arabic Documents Texts Datasets  
To validate the classification performance of the proposed ArcAR system for Arabic text documents, we use nine different datasets: Alkhaleej, Akhbarona-balance, Akhbarona-unbalance, Alarabiya-balance, Alarabiya-unbalance, BBC Arabic corpus, CNN Arabic corpus, Open Source Arabic Corpus (OSAC), and Arabic Influencer Twitter Dataset (AITD). The Alkhaleej, Akhbarona, and Alarabiya datasets were extracted from a large database called a single-label Arabic news article dataset (SANAD) [22]. The SANAD dataset is a large collection of Arabic news articles and can be used for Arabic NLP tasks, such as single-label Arabic text classification. The dataset is publically available at [[https://data.mendeley.com/datasets/572](https://data.mendeley.com/datasets/572) px667y9/1]([https://data.mendeley.com/datasets/572](https://data.mendeley.com/datasets/572) px667y9/1) (accessed on 15 July 2021) [22]. Table 1 shows the data distribution of the unbalanced SANAD datasets used in this study for each class. Moreover, the SANAD database has a balanced sub-dataset for Alkhaleej, Akhbarona, and Alarabiya [22]. The balanced datasets of Alkhaleej and Akhbarona collected and categorized seven different classes or categories: religion, finance, sports, culture, technology, politics, and medical. These datasets involve 6500 and 6700 Arabic text articles or documents for Alkhaleej and Akhbarona, respectively. Fortunately, as shown in Table 1, Alkhaleej has the same number of documents (i.e., 6500 Arabic articles) for each class for the balanced and unbalanced datasets. In addition, the articles of the Alkhaleej dataset were collected from the Alkhaleej news portal from 2008 until 2018, while the articles of the Akhbarona dataset were collected from the Akhbarona news portal from January 2011 until October 2018. Meanwhile, the Alarabiya balanced dataset has only five different categories: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#** & **Class Type** & **AlKhaleej** & **Akhbarona** & **Alarabiya** \\ \hline
1 & Finance & 6500 & 9280 & 30,076 \\
2 & Sports & 6500 & 15,755 & 23,058 \\
3 & Culture & 6500 & 6746 & 5619 \\
4 & Technology & 6500 & 12,199 & 4410 \\
5 & Politics & 6500 & 13,979 & 4368 \\
6 & Medical & 6500 & 12,947 & 3715 \\
7 & Religion & 6500 & 7552 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SANAD Dataset Distribution for each Class: Alkhaleej, Akhbarona, and Alarabiya. Figure 1: Schematic diagram of the proposed Arabic text computer-aided recognition (ArcAR) system. politics, finance, medical, sports, and technology. Unfortunately, the Alarabiya-balance dataset does not have any Arabic documents for the culture class. For studying aspects of culture using Alarabiya-balanced and unbalanced datasets, we add the culture class by randomly selecting Arabic documents from the unbalanced dataset. This is to perform an acceptable performance comparison of the ArcAD system using the same classes for both balanced and unbalanced Alarabiya datasets. The Alarabiya-balance dataset has six classes: politics, finance, medical, sports, technology, and culture. In this study, each class of the Alarabiya-balance dataset involves 3700 Arabic text documents. However, the Alarabiya dataset was collected from the main website of Al-Arabiya (i.e., [[https://www.alarabiya.net](https://www.alarabiya.net)]([https://www.alarabiya.net](https://www.alarabiya.net)) (accessed on 15 July 2021)), which has two subdomains: \"alhadath\" and \"aswaq\". On the other hand, the data distribution for unbalanced datasets, which belong to open source Arabic corpora such as BBC, CNN, and OSAC is explained in detail in the following sections. BBC Arabic Corpus  
This dataset was collected from the BBC website, [[https://www.bbc.com/arabic](https://www.bbc.com/arabic)]([https://www.bbc.com/arabic](https://www.bbc.com/arabic)) (accessed on 15 July 2021) and included 4763 Arabic text documents [52].