## 3 Methodology
### Deep Learning Approach
#### 3.6.3 The Proposed Arb-MCNN-Bi Model  
The Arb-MCNN-Bi model, illustrated in Figure 4, is a fusion of transformer-based architecture and deep learning techniques, specifically designed to enhance Arabic sentiment analysis. This model combines the strengths of the AraBERT model, which provides context-aware word embeddings for Arabic text, with the robust feature extraction capabilities of a MCNN and the sequential processing power of a BiGRU. (1) AraBERT: For feature extraction, this research employed AraBERT, a cutting-edge pre-trained language model renowned for its attention mechanism, which adeptly captures contextual word relationships. AraBERT is particularly powerful in deep learning models for sentiment analysis. The study utilized AraBERT's extensive configuration, featuring 768 hidden units and 12 attention heads, providing richer data and vocabulary compared to the base model. The initial step in our text classification process involves tokenization, transforming text into a machine-readable format using the WordPiece tokenizer. The original AraBERT tokenizer [40] was trained with WordPiece tokenization, allowing words to be split into multiple subwords. However, due to the morphological complexity of Arabic, character and subword tokenizers are not well-suited for Arabic text. Consequently, we utilized two different pre-trained Arabic BERT tokenizers that employ word-level tokenization, applying a specific tokenizer to each dataset based on its context: [PERSON] et al. [17]'s tokenizer for social media datasets like Arabic-Jordanian tweets, and [PERSON] et al. [18]'s general-purpose tokenizer for other datasets, such as Goodreads and Booking.com reviews. (2) Multichannel Convolutional Layers: To construct the multichannel convolutional architecture, three distinct convolutional layers were employed. Each layer is characterized by 128 filters and kernel sizes of 3, 4, and 5, along with the Rectified Linear Unit (Relu) activation function to address gradient issues. (3) Padding Application: Following the convolutional operations, padding was applied to the outputs of the second and third layers. This ensured that the dimensions of the outputs remained uniform, facilitating concatenation in subsequent stages of the neural network architecture. (4) Output Merging: The final step involved merging the outputs of the convolutional layers. This merging process provided a comprehensive feature extraction from the text data, which subsequent neural network layers could further utilize for accurate classification tasks. (5) Normalization and Pooling: To enhance model stability and prevent overfitting, batch normalization layers are incorporated after each convolutional layer. Additionally, max pooling 1D layers with appropriate pool sizes are introduced to effectively reduce the dimensionality of feature maps and enhance computational efficiency. Following the merging of feature maps from different convolutional layers, max pooling, utilizing a 2 \(\times\) 2 filter is executed. Max pooling serves multiple purposes within our neural network architecture. It significantly reduces dimensionality, improving computational efficiency and expediting data processing while retaining crucial features. This operation involves sliding the filter across the feature map and selecting the maximum value from each 2 \(\times\) 2 area. Consequently, significant information is preserved, and important patterns in  
Figure 3: The architecture of the Proposed BiGRU Model for Arabic sentiment analysis. the data are highlighted. The outcome is a pooled feature map with dimensions halved from the original, encapsulating essential features in a more concise format, ready for subsequent layers of the network. (6) BiGRU: The BiGRU architecture in our model consists of two stacked BiGRU layers, each with 128 units, designed to capture sequential dependencies in both directions.