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A Knowledge-Based Weighted KNN for Detecting Irony in Twitter

  • Delia Irazú Hernández FaríasEmail author
  • Manuel Montes-y-Gómez
  • Hugo Jair Escalante
  • Paolo Rosso
  • Viviana Patti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)

Abstract

In this work, we propose a variant of a well-known instance-based algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a challenging text classification task: irony detection. We experimented with corpora in the state of the art. The obtained results show that despite being a simple approach, our method is competitive with respect to more advanced techniques.

Keywords

Instance-based algorithm WKNN Irony detection 

Notes

Acknowledgments

This research was funded by CONACYT project FC 2016-2410. The work of P. Rosso has been funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. The work of V. Patti was partially funded by Progetto di Ateneo/CSP 2016 (IhatePrejudice, S1618_L2_BOSC_01).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Delia Irazú Hernández Farías
    • 1
    Email author
  • Manuel Montes-y-Gómez
    • 1
  • Hugo Jair Escalante
    • 1
  • Paolo Rosso
    • 2
  • Viviana Patti
    • 3
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)TonantzintlaMexico
  2. 2.PRHLT Research CenterUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.Dipartimento di InformaticaUniversity of TurinTurinItaly

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