The present work focuses on conventional deep learning models and introduces a proposed CNN–ResNet hybrid architecture for sentiment classification tasks. While traditional CNNs and RNNs have shown considerable success in NLP, they often struggle with capturing hierarchical semantic patterns. To address this, research integrates the deep feature extraction capability of ResNet into a CNN pipeline, improving contextual understanding and classification performance. Additionally, a diverse and labeled sentiment dataset and various domains, such as product reviews, social media, and general opinionated text has been used. The models were trained and evaluated on this dataset. Experimental results demonstrate that the proposed hybrid model achieves higher accuracy and robustness across multiple sentiment classes compared to baseline models. The work suggests that combining semantic richness with deep residual learning improves the reliability of sentiment classification systems.