Sentiment Analysis (SA) involves classifying text as positive, neutral, or negative. Arabic Sentiment Analysis (ASA) faces unique challenges due to the complexity of Arabic language features. To address these challenges, deep learning (DL), a branch of machine learning (ML), employs various neural network (NN) models. This research evaluates the effectiveness of key NN models in ASA, including Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Attention Mechanisms, and Transformer-based models. CNNs excel in ASA by automatically extracting relevant features from text with minimal preprocessing, making them suitable for tasks like educational content analysis. RNNs, particularly LSTM and GRU, handle sequential data effectively, capturing the context and nuances in long Arabic texts. Attention mechanisms enhance RNNs by focusing on relevant text parts, improving accuracy. By leveraging these mechanisms, transformer-based models set a new standard in ASA, processing text in parallel and capturing complex word relationships with exceptional performance. Our analysis shows that CNNs and LSTM models are highly effective for ASA. Transformer-based models are emerging as the leading choice for superior accuracy in ASA.