Enhancing <scp>AG</scp> News Classification With Hypergraph Attention Networks and Quadratic <scp>SVM</scp>

ABSTRACT News text classification is a technique of classifying news articles into some predefined classes. It helps consumers find news that piques their interest. Due to the growth of internet news content, effective automatic classification systems are required to handle and arrange massive volumes of news articles. Here, the AG's News Corpus (AG News) articles are classified through the hypergraph neural network along with the attention layer and quadratic support vector machine (AGNews_HAL_QSVM). This benchmark dataset was named after the ‘ComeToMyHead’ project by Alberto G. (AG) Leonardo. The dataset was gathered from Kaggle, and the LDA (Latent Dirichlet Allocation) was used to generate the topic‐specific data. Every topic will be regarded as a hyperedge in the hypergraph, and each topic's words will be regarded as a hypervertex. A hypergraph convolution neural network with an attention layer is used to extract the corpus' key features. For classification, the collected features are sent into a quadratic support vector machine. A complex deep‐learning model has been used to test the proposed model. At an accuracy of 91.2%, the suggested model performs better than the other state‐of‐the‐art algorithms. In order to improve automatic AGNews classification systems, this study presents a practical implementation of the proposed model for organising news content. It propels developments in public discourse, media, personalisation and policy.