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Language Processing and Knowledge in the Web pp 176–183Cite as

Data-Driven vs. Dictionary-Based Word n-Gram Feature Induction for Sentiment Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8105))

Abstract

We address the question which word n-gram feature induction approach yields the most accurate discriminative model for machine learning-based sentiment analysis within a specific domain: a purely data-driven word n-gram feature induction or a word n-gram feature induction based on a domain-specific or domain-non-specific polarity dictionary. We evaluate both approaches in document-level polarity classification experiments in 2 languages, English and German, for 4 analog domains each: user-written product reviews on books, DVDs, electronics and music. We conclude that while dictionary-based feature induction leads to large dimensionality reductions, purely data-driven feature induction yields more accurate discriminative models.

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Author information

Authors and Affiliations

  1. Natural Language Processing Group, Department of Computer Science, University of Leipzig, Germany

    Robert Remus

  2. Goethe University Frankfurt, Germany

    Sven Rill

  3. Institute of Information Systems, University of Applied Sciences Hof, Hof, Germany

    Sven Rill

Authors
  1. Robert Remus
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  2. Sven Rill
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Editor information

Editors and Affiliations

  1. Technical University Darmstadt, 64289 Darmstadt, Germany, and German Institute for International Education Research,, 60486, Frankfurt, Germany

    Iryna Gurevych

  2. Technical University Darmstadt, 64289, Darmstadt, Germany

    Chris Biemann

  3. Technical University Darmstadt, 64289 Darmsadt, and German Institute for International Educational Research, 60486, Frankfurt, Germany

    Torsten Zesch

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Remus, R., Rill, S. (2013). Data-Driven vs. Dictionary-Based Word n-Gram Feature Induction for Sentiment Analysis. In: Gurevych, I., Biemann, C., Zesch, T. (eds) Language Processing and Knowledge in the Web. Lecture Notes in Computer Science(), vol 8105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40722-2_18

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  • DOI: https://doi.org/10.1007/978-3-642-40722-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40721-5

  • Online ISBN: 978-3-642-40722-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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