This study explores hybrid deep learning architectures for the classification of financial sentiment, focusing on the integration of the Convolutional Neural Network (CNN) with the Support Vector Machine (SVM) and the Random Forest (RF). CNN, with its powerful feature extraction capabilities, was combined with SVM’s ability to handle non-linear decision boundaries, while RF enhanced model generalization through ensemble learning. The proposed hybrid frameworks addressed two fundamental challenges in sentiment analysis: overfitting and class imbalance. These challenges were mitigated, resulting in improved model accuracy and reliability compared to standalone methods. Empirical evaluations demonstrated that the CNN-SVM model achieved competitive or superior validation accuracy and loss, indicating its suitability for precise financial sentiment classification. By enabling more accurate sentiment categorization, the model provides actionable insights for financial analysts and investors, thereby supporting better market assessment and investment decision-making. Future work is suggested to incorporate advanced techniques such as adversarial training and domain-specific pre-trained models to further enhance model performance.
