Text classification is a crucial research area in natural language processing, particularly in handling customer reviews and feedback for products. Organizations seek prompt analysis of this textual data to take swift actions. Manual reviews are time-consuming and resource-intensive, potentially impacting product sales. To address this challenge, organizations are turning to machine learning algorithms implemented by their IT departments for real-time text processing. Gated Recurrent Units (GRUs) represent an advancement in Recurrent Neural Networks (RNNs) and function as a gating mechanism in the network. GRU networks excel in tasks involving sequential learning, addressing challenges such as gradient vanishing and explosion commonly encountered in ordinary RNNs when capturing long-term dependencies. In this study, we introduced an enhanced text categorization approach that deviates from the conventional approach by substituting a linear Support Vector Machine (SVM) with Softmax in the final output layer of a GRU network. Additionally, we replace the cross-entropy function with a margin-based alternative. The primary focus is utilizing the Gated Recurrent Units method, specifically designed to embed fixed-size matrix text, to inform the network about long-term dependencies. Empirical findings indicate that the developed GRU-SVM approach outperformed than state-of-the-art approaches, specifically BLSTM-C and DABN, regarding achieved results.