A Natural Language Processing-Based Multimodal Deep Learning Approach for News Category Tagging

With the rise in the amount of news available today, the need for its classification has emerged. In this paper, we present methods for tagging news categories using different deep learning models along with a comparison of their effects. These models include single-channel CNN model, multichannel CNN model, and multimodal CNN model. This study involves integration of natural language understanding with convolutional methods that understands descriptions, titles, and tags to enhance news ranking. The novel part of this approach is to find out using natural language understanding with the transfer learning from the supplemental external features that are associated with images. The accuracy of the single-channel model was found to be 81.30%, of the multi-channel model was 85.98% and that of the multi-modal model was 85.39%. We have used the N24 news dataset for the validation of the models.