## 1 Introduction  
The importance of automatic tweet or text classification has increased with the ongoing addition of text-based content to the internet. The rapid growth of social and digital media has resulted in an ever-increasing amount of data generated by various sources. The availability of such unorganized data provides an enormous potential for processing and extracting valuable information. One critical task is identifying individuals depending on their tweeting and grouping, a field of study that has gained increasing interest in recent years. Automated multi-labeled systems are now possible because of recent advances in deep-learning- and machine-learning-based approaches. Tweet or text classification is a necessary condition for the development of various emerging applications in diverse fields, such as linguistic (and dialect) recognition, user's tweet classification, sentiment analysis, genre classification, spam filtering, and a few more [1; 2; 3; 4]. There is a significant need for an automatic tweet or text categorization system for Arabic due to the abundance of Arabic data available on the web nowadays. There are different criteria for classifying Arabic tweets, depending on the accessibility of data, resource limitations, and data quality. The Arabic language is considered one of the most widely used languages on social media, especially Twitter and Facebook. Most user and people attitudes are reflected on social media to express their opinions to different news, topics, and provided services that can be enhanced in future. Furthermore, the Arabic language on social media platforms contains long tweets with different morphological and polarity terms that are difficult to analyze. As presented in [5], the unsupervised sentiment analysis method is applied to improve the performance of analyzing sentiments using term weights from different datasets. In addition, the features are extracted for predicting the target or objective features. The authors of [6] proposed different machine-learning models to classify text and provide a recommender system that can predict the orientation of sentiments. Despite this, they frequently fail to consider textual context or word order, so their classification accuracy needs to be improved by data sparsity. Furthermore, deep-learning-based techniques have been proven more efficient than conventional machine-learning approaches. They can extract important characteristics effectively without requiring extensive artificial feature extraction [7]. The RNN and CNN have demonstrated varying capacities in displaying text. RNNs are good at modeling sequential data and effectively representing text by understanding contextual aspects and long-term relationships in phrases. They have been successfully employed in natural language-processing applications [8]. Some known algorithms for text categorization combine CNN and RNN to maximize their potential [9]. However, these algorithms treat every word in the phrase equally, making it impossible to tell which keywords are more important in the categorization process than the more prevalent ones. According to the attention-based technique, neural networks may give varying weights to words in a sentence based on their value for categorization, alleviating the previous difficulties. Attention-based RNN, CNN, and GRU can produce state-of-the-art results. However, these techniques are attributed to individual CNN, GRU, or RNN, and the significance of learning contextual information is insignificant in their implementation [10]. The extent of BERT's potential has yet to be explored, even though it has given reasonable results in NLP-related tasks surpassing most conventional approaches, such as word2 vec, Cove, and Glove. Incorporating BERT does not help much because it is trained using a blend of a corpus in English and has no text categorization skills. When there is insufficient internal training data for fine-tuning, a task-awareness problem may be solved by using an external domain-related corpus.