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Evaluating the Impact of Syntax and Semantics on Emotion Recognition from Text

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7817))

Abstract

In this paper, we systematically analyze the effect of incorporating different levels of syntactic and semantic information on the accuracy of emotion recognition from text. We carry out the evaluation in a supervised learning framework, and employ tree kernel functions as an intuitive and effective way to generate different feature spaces based on structured representations of the input data. We compare three different formalisms to encode syntactic information enriched with semantic features. These features are obtained from hand-annotated resources as well as distributional models. For the experiments, we use three datasets annotated according to the same set of emotions. Our analysis indicates that shallow syntactic information can positively interact with semantic features. In addition, we show how the three datasets can hardly be combined to learn more robust models, due to inherent differences in the linguistic properties of the texts or in the annotation.

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Author information

Authors and Affiliations

  1. FBK-irst, Trento, Italy

    Gözde Özbal

  2. UPC, Barcelona, Spain

    Daniele Pighin

Authors
  1. Gözde Özbal
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  2. Daniele Pighin
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Mexico D.F., Mexico

    Alexander Gelbukh

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Özbal, G., Pighin, D. (2013). Evaluating the Impact of Syntax and Semantics on Emotion Recognition from Text. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-37256-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37255-1

  • Online ISBN: 978-3-642-37256-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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