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A System for Automatic Classification of Twitter Messages into Categories

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Modeling and Using Context (CONTEXT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9405))

Abstract

Twitter is a widely used online social networking site where users post short messages limited to 140 characters. The small length of these messages is a challenge when it comes to classifying them into categories. In this paper we propose a system that automatically classifies Twitter messages into a set of predefined categories. The system takes into account not only the tweet text, but also external features such as words from linked URLs, mentioned user profiles, and Wikipedia articles. The system is evaluated using various combinations of feature sets. According to our results, the combination of feature sets that achieves the highest accuracy of 90.8 % is when the original tweet terms are combined with user profile terms along with terms extracted from linked URLs. Including terms from Wikipedia pages, found specifically for each tweet, is shown to decrease accuracy for the original test set, however accuracy was shown to increase using a fraction of the original test set containing only tweets without URLs.

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Notes

  1. 1.

    The NReadability library is used to take only the article text from a specific HTML file ignoring irrelevant text such as sidebar text containing other stories. https://github.com/marek-stoj/NReadability [Online; accessed 6-June-2015].

  2. 2.

    https://tweetinvi.codeplex.com [Online; accessed 6-June-2015].

  3. 3.

    The database contains every Wikipedia page title from the English Wikipedia, originally taken from the April 2015 Wikipedia dump. https://dumps.wikimedia.org/enwiki/20150304/ [Online; accessed 6-June-2015].

  4. 4.

    https://tweetinvi.codeplex.com [Online; accessed 6-June-2015].

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Nicosia, 1700, Nicosia, Cyprus

    Alexandros Theodotou & Athena Stassopoulou

Authors
  1. Alexandros Theodotou
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  2. Athena Stassopoulou
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Corresponding author

Correspondence to Athena Stassopoulou .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  2. ENS-Pavillon Jardin, CNRS-Institut Jean-Nicod, Paris, France

    Isidora Stojanovic

  3. Department of Computer Science, University of Cyprus, Aglantzia, Cyprus

    George A. Papadopoulos

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Theodotou, A., Stassopoulou, A. (2015). A System for Automatic Classification of Twitter Messages into Categories. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-25591-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25590-3

  • Online ISBN: 978-3-319-25591-0

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