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

  • Gözde Özbal
  • Daniele Pighin
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Emotion Recognition Semantic Feature Latent Semantic Analysis Emotion Category Fairy Tale 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gözde Özbal
    • 1
  • Daniele Pighin
    • 2
  1. 1.FBK-irstTrentoItaly
  2. 2.UPCBarcelonaSpain

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