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Detecting Implicit Emotion Expressions from Text Using Ontological Resources and Lexical Learning

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New Trends of Research in Ontologies and Lexical Resources

Abstract

In the past years, there has been a growing interest in developing computational methods for affect detection from text. Although much research has been done in the field, this task still remains far from being solved, as the presence of affect is only in a very small number of cases marked by the presence of emotion-related words. In the rest of the cases, no such lexical clues of emotion are present in text and special commonsense knowledge is necessary in order to interpret the meaning of the situation described and understand its affective connotations. In the light of the challenges posed by the detection of emotions from contexts in which no lexical clue is present, we proposed and implemented a knowledge base – EmotiNet – that stores situations in which specific emotions are felt, represented as “action chains”. Following the initial evaluations, in this chapter, we describe and evaluate two different methods to extend the knowledge contained in EmotiNet: using lexical and ontological knowledge. Results show that such types of knowledge sources are complementary and can help to improve both the precision, as well as the recall of implicit emotion detection systems based on commonsense knowledge.

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Notes

  1. 1.

    http://www.cyc.org

  2. 2.

    http://www.ontologyportal.org/

  3. 3.

    http://www.unige.ch/fapse/emotion/databanks/isear.html

  4. 4.

    For 11 examples, the Semantic Role Labeling system employed – proposed by Moreda et al. [25] had a void output.

  5. 5.

    http://wefeelfine.org/api.html

  6. 6.

    http://wefeelfine.org/mission.html

  7. 7.

    http://api.wefeelfine.org:8080/ShowFeelings?display=xml&returnfields=sentence&postyear=2004&feeling=happy&limit=1500

  8. 8.

    http://rtw.ml.cmu.edu/rtw/

  9. 9.

    http://www.bing.com/developers/s/APIBasics.html

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Acknowledgements

The work by Jesús M. Hermida has been supported by the Spanish Ministry of Education under the FPU Program (ref. AP2007-03076).

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Authors and Affiliations

  1. European Commission Joint Research Centre, Via E. Fermi 2749, 21027, Ispra (VA), Italy

    Alexandra Balahur & Hristo Tanev

  2. Department of Software and Computing Systems, University of Alicante, Ap. de Correos 99, 03080, Alicante, Spain

    Jesús M. Hermida

Authors
  1. Alexandra Balahur
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  2. Jesús M. Hermida
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  3. Hristo Tanev
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Corresponding author

Correspondence to Alexandra Balahur .

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Editors and Affiliations

  1. Psychology Department, Carnegie Mellon University, Forbes Avenue 5000, Pittsburgh, 15213, Pennsylvania, USA

    Alessandro Oltramari

  2. Faculty of Arts, Language, Cognition and, Vrije University Amsterdam, De Boelelaan 1105, Amsterdam, 1081, Netherlands

    Piek Vossen

  3. Department of Computing, Hong Kong Polytechnic University, PQ806 Mong Man Wai Building, Hong Kong, 999077, Hong Kong SAR

    Lu Qin

  4. Language Technologies Institute, Carnegie Mellon University, Forbes Avenue 5000, Marina Del Rey, 15213, Pennsylvania, USA

    Eduard Hovy

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Balahur, A., Hermida, J.M., Tanev, H. (2013). Detecting Implicit Emotion Expressions from Text Using Ontological Resources and Lexical Learning. In: Oltramari, A., Vossen, P., Qin, L., Hovy, E. (eds) New Trends of Research in Ontologies and Lexical Resources. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31782-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-31782-8_12

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