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News Articles Classification Using Random Forests and Weighted Multimodal Features

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8849)

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.

Keywords

Document classification Supervised learning Multimodal News articles N-gram features Random Forests Visual features Fusion 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Information Technologies Institute, Centre for Research and Technology HellasThermi-ThessalonikiGreece
  2. 2.Dept. of Computer ScienceJerusalem College of Technology - Lev Academic CenterJerusalemIsrael

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