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Annotating and Identifying Emotions in Text

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Intelligent Information Access

Part of the book series: Studies in Computational Intelligence ((SCI,volume 301))

Abstract

This paper focuses on the classification of emotions and polarity in news headlines and it is meant as an exploration of the connection between emotions and lexical semantics. We first describe the construction of the data set used in evaluation exercise “Affective Text” task at SemEval 2007, annotated for six basic emotions: anger, disgust, fear, joy, sadness and surprise, and for positive and negative polarity. We also briefly describe the participating systems and their results. Second, exploiting the same data set, we propose and evaluate several knowledge-based and corpus-based methods for the automatic identification of emotions in text.

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Author information

Authors and Affiliations

  1. FBK-IRST,  

    Carlo Strapparava

  2. University of North Texas,  

    Rada Mihalcea

Authors
  1. Carlo Strapparava
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  2. Rada Mihalcea
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Editor information

Editors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari Piazza d’Armi , I09123, Cagliari, Italy

    Giuliano Armano  & Eloisa Vargiu  & 

  2. Department of Informatics, University of Bari “Aldo Moro” , Via E. Orabona, 4, 70126, Bari, Italy

    Marco de Gemmis

  3. Department of Informatics, University of Bari “AldoMoro” , Via E. Orabona, 4, 70126, Bari, Italy

    Giovanni Semeraro

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Strapparava, C., Mihalcea, R. (2010). Annotating and Identifying Emotions in Text. In: Armano, G., de Gemmis, M., Semeraro, G., Vargiu, E. (eds) Intelligent Information Access. Studies in Computational Intelligence, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14000-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-14000-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13999-4

  • Online ISBN: 978-3-642-14000-6

  • eBook Packages: EngineeringEngineering (R0)

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