## 1 Introduction
To overcome the limitations of the aforementioned models, we proposed a model ARABERT4 TWC for Arabic tweet classification. We attained performance compared to other well-known techniques, with three out of five datasets used. First, data sanitation is performed on the datasets to remove Arabic stop words and punctuation. As a part of data preprocessing, the model input is first tokenized with a word-piece tokenizer trained on a large Arabic corpus that contains 1.5-billion Arabic corpora and OSIAN, which are publicly available [11; 12]. The word-piece tokenizer is based on a sub-word tokenization algorithm. Sub-word tokenization techniques are based on the idea that commonly employed phrases must be distinct from shorter sub-words. However, unique words should be deconstructed into meaningful sub-words when tokenizing them. Sub-word tokenization helps the model acquire meaningful, contextually independent representations while maintaining a suitable lexicon size. The tokens obtained from the word-piece tokenizer are combined with segment and positional embedding. Second, an Arabic BERT for tweet classification is constructed by fine-tuning the pre-trained BERT case model provided by the Hugging Face Transformer Library that has been trained on larger unlabeled text corpora, including book corpora and Wikipedia [13; 14; 15]. The fine-tuning is executed by initializing the weights of the pre-trained BERT model. All the 109-million parameters are fine-tuned with labeled data. A dense classification layer is added to the encoder to obtain an Arabic BERT, specifically used for Arabic tweet classification. The custom BERT model consists of 12 encoder layers with 12 self-attention heads, which are bi-directional, and 12 hidden layers. The final embedding layers obtained after merging with segment and positional embedding are passed through the encoder structure. Finally, the Softmax classification layer is used for classifying Arabic tweets. To validate our proposed model, the model's performance in terms of accuracy is evaluated using five benchmark datasets with other well-known existing models for tweet classification. The contribution of the paper is explained as follows:  
1. Propose an ARABERT4 TWC model for classifying Arabic long tweets into different categories. 2. Propose a tokenization process algorithm for measuring the frequency of each pair of text in the vocabulary and then determining the most frequent terms. 3. Proposing an ARABERT4 TWC algorithm that contains a token, segment, and positional embedding of terms, then applying an encoder/transformer. 4. Applying a fine-tuning of the ARABERT4 TWC model ARABERT4 TWC to achieve better performance on social media-related tasks. 5. Applying the proposed model to different datasets and the experimental results are compared with recent research methodologies, which explored high accuracy in the proposed model. The remaining structure of the paper is as follows: The related work in text or tweet classification is reported in Section 2. The proposed methodology Arabic BERT model for tweet classification (ARABERT4 TWC) is presented in Section 3, which explains the sanitation of data, the model architecture, the proposed algorithms, the self-attention layer, and the classification layer. The experiment setup, performance evaluation of our proposed model, and a comparison of the existing techniques are explained in Section 4. Section 5 summarizes the conclusion and future scope.