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Verbs Speak Loud: Verb Categories in Learning Polarity and Strength of Opinions

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Advances in Artificial Intelligence (Canadian AI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5032))

Abstract

We show that verbs reliably represent texts when machine learning algorithms are used to learn opinions. We identify semantic verb categories that capture essential properties of human communication. Lexical patterns are applied to construct verb-based features that represent texts in machine learning experiments. Our empirical results show that expressed actions provide a reliable accuracy in learning opinions.

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Authors and Affiliations

  1. Département d’informatique et de recherche opérationnelle, Université de Montréal,  

    Marina Sokolova & Guy Lapalme

Authors
  1. Marina Sokolova
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  2. Guy Lapalme
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Editor information

Sabine Bergler

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© 2008 Springer-Verlag Berlin Heidelberg

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Sokolova, M., Lapalme, G. (2008). Verbs Speak Loud: Verb Categories in Learning Polarity and Strength of Opinions. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_30

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  • DOI: https://doi.org/10.1007/978-3-540-68825-9_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68821-1

  • Online ISBN: 978-3-540-68825-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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