The Role of Deep Neural Networks in Improving Arabic Text Analysis

دور الشبكات العصبية العميقة في تحسين تحليل النصوص العربية

Abstract

Deep neural networks have demonstrated significant effectiveness in advancing Arabic text analysis across various applications such as sentiment analysis, machine translation, and text generation. Models like LSTM, GRU, and Transformer-based architectures provide enhanced capabilities for processing Arabic text by capturing intricate syntactic and semantic structures. Despite these advancements, challenges persist, including the demand for extensive linguistic resources, high computational requirements, and the complexity of dealing with diverse Arabic dialects. This article examines how deep neural networks address these challenges and improve Arabic text analysis. Case studies reveal notable improvements in machine translation and sentiment analysis, showing a marked increase in performance compared to traditional methods. To tackle these issues, recommendations include developing open-source linguistic resources, enhancing model efficiency for less resource-intensive devices, and creating dialect-specific models. Ongoing research is crucial for further boosting model performance and ensuring Arabic text analysis becomes more robust and accessible.