دور الشبكات العصبية العميقة في تحسين تحليل النصوص العربية

The Role of Deep Neural Networks in Improving Arabic Text Analysis

Abstract

Deep neural networks have proven to be highly effective in improving Arabic text analysis through various applications such as sentiment analysis, machine translation, and text generation. These models, particularly LSTM, GRU, and Transformer-based architectures, offer advanced capabilities in processing Arabic texts by understanding complex syntactic and semantic structures. However, challenges remain, including the need for large linguistic resources, high computational costs, and the difficulty of handling the diverse Arabic dialects. The article explores the role of deep neural networks in overcoming these challenges and enhancing text analysis in Arabic. Case studies show improvements in machine translation and sentiment analysis, indicating a significant increase in performance accuracy compared to traditional methods. Recommendations for addressing these challenges include creating open-source linguistic resources, improving model efficiency for less costly devices, and developing dialect-specific models. Future research efforts are essential to enhance model performance and make Arabic text analysis more robust and accessible.