## 1 Introduction  
Sentiment analysis is critical for understanding customer sentiments about products and services, providing companies with valuable insights that can enhance customer satisfaction, improve brand image, and boost profitability [1]. As more people express their opinions online, robust sentiment analysis tools become increasingly vital, particularly in the context of large volumes of user-generated content on platforms like Twitter, Facebook, and e-commerce websites. Sentiment analysis, also known as opinion mining, involves extracting subjective information from text, and interpreting attitudes, emotions, and sentiments expressed through written or spoken language. This process enables organizations to understand public perception, track brand reputation, identify trends, and make data-driven decisions using machine learning (ML) and deep learning (DL) algorithms. While sentiment analysis is well-established in languages like English and Chinese, Arabic presents unique challenges due to its intricate morphology, diverse dialects, and complex syntax [2]. Traditional machine learning approaches often struggle to accurately interpret Arabic text, particularly with context-dependent meanings, morphological complexities,and nuanced expressions of sentiment, including negations and ambiguities. Addressing these challenges is essential for improving the accuracy and applicability of sentiment analysis in Arabic. Moreover, recent advancements in deep learning, particularly in technologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), have revolutionized text analysis by effectively extracting features and capturing intricate patterns in text data [3; 4]. These models excel in learning hierarchical representations, making them highly effective in text classification tasks, including sentiment analysis in Arabic. Furthermore, the integration of contextualized word embeddings from models like Word2 Vec, fastText, and Arabic Bidirectional Encoder Representations from Transformers (AraBERT) has significantly enhanced the performance of DL models in Arabic sentiment analysis [5; 6]. These embeddings encode semantic nuances and contextual relationships in text, thereby improving the accuracy and efficiency of sentiment classification tasks. Additionally, transformer models, such as BERT, have gained significant popularity due to their ability to capture contextual relationships within sentences through self-attention mechanisms, allowing for a better understanding of context and enhancing model performance. The attention mechanism in transformers allocates different levels of importance to various parts of the input, revolutionizing the processing of long sequential data, leading to notable improvements in accuracy and result quality. This research aims to further advance Arabic sentiment analysis by leveraging these advanced deep learning architectures and methodologies, addressing the complexities of the Arabic language and its diverse dialects [7; 8]. In this paper, we propose the Arb-MCNN-Bi Model (which combines AraBERT, a Multi-channel Convolutional Neural Network (MCNN), and a Bidirectional Gated Recurrent Unit (BiGRU)). This model integrates the strengths of the transformer-based AraBERT model with an MCNN and a BiGRU. The proposed approach consists of three stages: text preprocessing and data cleaning, extracting sentence-level semantics and contextual features using the pre-trained AraBERT model, and feeding the contextually embedded vectors into a neural network. To further enhance sentiment analysis, we incorporate various word embedding techniques, such as Word2 Vec and fastText, alongside advanced architectures, including the MCNN and BiGRU framework. Each of these components is carefully designed to improve model generalization and effectively process sequential and contextual text data, which is critical for Arabic sentiment analysis.