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News Articles Classification Using Random Forests and Weighted Multimodal Features

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Multidisciplinary Information Retrieval (IRFC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8849))

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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.

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Author information

Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thermi-Thessaloniki, Greece

    Dimitris Liparas, Anastasia Moumtzidou, Stefanos Vrochidis & Ioannis Kompatsiaris

  2. Dept. of Computer Science, Jerusalem College of Technology - Lev Academic Center, 21 Havaad Haleumi St., P.O.B. 16031, 9116001, Jerusalem, Israel

    Yaakov HaCohen-Kerner

Authors
  1. Dimitris Liparas
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  2. Yaakov HaCohen-Kerner
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  3. Anastasia Moumtzidou
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  4. Stefanos Vrochidis
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  5. Ioannis Kompatsiaris
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Tallinn University, Narva mnt 25, Tallinn, Estonia

    David Lamas

  2. Insight Centre for Data Analytics, Unit for Natural Language Processing, IDA, National University of Ireland, Business Park, Lower Dangan, Galway, Ireland

    Paul Buitelaar

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Liparas, D., HaCohen-Kerner, Y., Moumtzidou, A., Vrochidis, S., Kompatsiaris, I. (2014). News Articles Classification Using Random Forests and Weighted Multimodal Features. In: Lamas, D., Buitelaar, P. (eds) Multidisciplinary Information Retrieval. IRFC 2014. Lecture Notes in Computer Science, vol 8849. Springer, Cham. https://doi.org/10.1007/978-3-319-12979-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-12979-2_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12978-5

  • Online ISBN: 978-3-319-12979-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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