# BERT Models for Arabic Text Classification: A Systematic Review
## 4 Discussion
### How Effective Are They Compared to the Original English BERT Models?  
The performance of the Arabic BERT models, as was found in the reviewed articles, is no different than the performance of the original English BERT model. The highest performance on a binary classification was scored by AraBERT (accuracy = 0.997, F1 Score = 0.981) on a dataset of 134,222 tweets classified as either spam or ham. The highest performance on a multi-class classification was also scored by AraBERT (accuracy = 0.99, F1 Score = 0.99) on a dataset of 22,429 articles classified by topics, e.g., economic, science, and low. Close results were reported for the English language in a comprehensive review conducted by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [70]. They reported that the BERT-base model was able to achieve an accuracy score of 0.981 on the Yelp dataset that contained around 600,000 businesses reviews classified into two classes, i.e., negative and positive. For multi-class classification, [PERSON] et al. reported that the BERT- large model scored an accuracy score of 0.993 on the DBpedia dataset that contained around 630,000 items, each with a 14-class label.  
This excellent performance was somewhat surprising considering the fact that the Arabic language is normally classified as a low-resource language that does not have a large amount of resources available on the web [74; 75; 76]. It seems that this classification is not valid anymore. The Developers of the Arabic BERT models were able to find billions of tokens of text to train their models. Wikipedia and news websites provided a plethora of text for training. The social media platform Twitter was also a major source of data. According to the latest report from Statista [77], two Arabic countries, i.e., Saudi Arabia and Egypt, are among the top leading countries based on the number of Twitter users as of January 2022.  
Despite the excellent performance of Arabic BERT models compared to the original English BERT, two shortcomings of the included studies have to be highlighted. First, while English BERT was evaluated by a variety of long and short text corpus [70; 78], the vast majority of the datasets that were used to evaluate the performance of the Arabic BERT models had short text corpus. In fact, only two datasets had a long text corpus. Therefore, the data that were found in the reviewed articles were not sufficient to judge the performance of Arabic BERT models when dealing with documents that had large text. According to [PERSON], et al. [79], short text has unique features that differ from long text including the syntactical structure, sparse nature, noise words, and the use of colloquial terminologies. The second shortcoming of the included studies is related to the type of data in the evaluation datasets. Unlike English BERT which has been evaluated with datasets from different knowledge domains [70; 71], the vast majority of the datasets that have been used to evaluate Arabic BERT models had tweets extracted from the Twitter platform. Therefore, it is not clear whether the current Arabic BERT models are suitable to be applied in a variety of application domains.