Enhancing Text Classification with an Attention-Integrated CNN-SVM Hybrid Model

The proposed text classification framework employs CNN as well as attention mechanisms alongside SVM to build a hybrid structure which supports improved classification operations. This model makes use of CNN networks to extract hierarchical deep features from texts and adds attention mechanisms to spot important elements in the input so features become more interpretable and better represented. The enriched features enter an SVM classifier due to its exceptional capability to create optimal decision boundaries for precise classification results. Text classification faces strong challenges because data imbalance causes performance problems that reduce model generality. The Attention-Integrated CNN-SVM Hybrid Model applies methods which reduce data imbalance while ensuring that the selected features are both descriptive and discriminative. This joint approach simultaneously boosts the educational outcomes as well as the accuracy rates achieved by the system. The combined feature extraction with attention mechanisms from SVM classification results in substantial improvements of both model operational speed and predictive effectiveness. The hybrid model provides an extensive solution for efficient text classification through its combination of deep learning and traditional machine learning methods to achieve rapid and precise and stable results on various textual documents.