Sentiment analysis, a natural language processing task, has gained significant attention because of its diverse applications in understanding user opinions and emotions in social media, customer feedback, and online reviews. Several studies have been conducted on this task using English datasets, yielding noteworthy outcomes. However, it is important to note that research on the same task for the Vietnamese language remains limited, and the available training data are currently not substantial. The proposed hybrid model is based on the power of two architectures: RoBERTa, a transformer-based model, and Bidirectional Gated Recurrent Units (Bi-GRU). By fusing the strengths of both models, the approach aims to enhance sentiment analysis performance and generalize better on diverse datasets. The research outlines the model's architecture, highlighting the seamless integration of RoBERTa and Bi-GRU components, and describes the fine-tuning process on a large corpus of Vietnamese texts for sentiment analysis. To evaluate the model's performance, comprehensive experiments are conducted on the benchmark datasets, comparing it with stateof-the-art approaches for sentiment analysis. The experimental results unequivocally demonstrate that the proposed model outperforms other methods across various metrics, including accuracy, precision, recall, and F1-score on both the IMDb and UIT-VSFC datasets