An Efficient Short Text Classification Model Based on Feature Shuffling and Attention-Enhanced With Multiview Contrastive Learning

Abstract

Short text classification remains a challenging task due to semantic sparsity, limited contextual information and the susceptibility of graph-based encodings to structural noise. Existing graph neural network and contrastive learning approaches partially mitigate these issues. They enrich contextual representations through neighbourhood aggregation and reduce structural noise through consistency constraints on representations. However, they still struggle to model heterogeneous semantic relations and often produce unstable multiview representations. To address these limitations, this paper proposes an efficient short text classification model based on Feature Shuffling and Attention-enhanced Multiview Contrastive Learning (FSAMCL). The method constructs heterogeneous graphs incorporating words, entities and part-of-speech tags, which are encoded using graph convolutional networks, while a term–document matrix models global semantic distributions. Feature-wise attention is applied to recalibrate node representations by amplifying salient semantic dimensions and suppressing irrelevant features. To enhance robustness, feature shuffling introduces lightweight, semantically coherent perturbations and singular value decomposition–based noise decomposition extracts low-rank semantic structures to generate noise-resistant augmented views. Cross-view attention then aligns and selectively integrates complementary information across heterogeneous views. Leveraging these enhanced representations, the framework jointly optimizes three contrastive objectives that reinforce invariance to perturbations, improve semantic discriminability and enhance cross-view consistency. Experiments on four benchmark datasets demonstrate the effectiveness of FSAMCL, with the largest performance gain observed on the Twitter dataset, where the model surpasses the previous best method by 3.09% in accuracy and 3.27% in F1 score. These findings highlight the contribution of FSAMCL as a robust, noise-tolerant and semantically enriched solution for short text classification.