News articles classification using random forests and weighted multimodal features

Abstract

This research investigates the problem of news articles classification. The classification is performed using N-gram textual features extracted from text and visual features generated from one representative image. The application domain is news articles written in English that belong to four categories: Business-Finance, Lifestyle-Leisure, Science-Technology and Sports downloaded from three well-known news web-sites (BBC, Reuters, and TheGuardian). Various classification experiments have been performed with the Random Forests machine learning method using N-gram textual features and visual features from a representative image. Using the N-gram textual features alone led to much better accuracy results (84.4%) than using the visual features alone (53%). However, the use of both N-gram textual features and visual features led to slightly better accuracy results (86.2%). The main contribution of this work is the introduction of a news article classification framework based on Random Forests and multimodal features (textual and visual), as well as the late fusion strategy that makes use of Random Forests operational capabilities.