Sequential Modeling of News Headlines and Descriptions for Multi-Class Classification

Abstract

Automatic classification of news content plays a vital role in organizing and filtering data for various applications such as news recommendation systems and media monitoring. This study investigates the use of Recurrent Neural Networks (RNN) and sequential modeling for multi-class classification of news data. A dataset consisting of 12,000 news sentences, categorized into four distinct classes politics, economy, sports, and technology was utilized for training and evaluation. The research focuses on comparing the performance of RNN models without optimization techniques and RNNs enhanced through optimizer implementation and sequence modeling. The baseline RNN model, trained without any optimizer or sequence enhancements, achieved a classification accuracy of 89%. By incorporating optimizer functions and leveraging sequential dependencies in both news headlines and descriptions, the proposed model demonstrated a 1% improvement, achieving an overall accuracy of 90%. These findings indicate that even a slight enhancement in modeling temporal dependencies and optimization can result in measurable gains in multi-class classification performance. The sequential combination of news headlines and descriptions is shown to be an effective strategy for capturing contextual features that improve the model’s predictive accuracy. This research contributes to the field of natural language processing by highlighting the effectiveness of sequential modeling and optimization in neural network-based text classification systems.