IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i11p2075-d448299.html
   My bibliography  Save this article

Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Author

Listed:
  • Óscar Apolinario-Arzube

    (Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Cdla, Universitaria Salvador Allende, Guayaquil 090514, Ecuador
    These authors contributed equally to this work.)

  • José Antonio García-Díaz

    (Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain
    These authors contributed equally to this work.)

  • José Medina-Moreira

    (Facultad de Ciencias Agrarias, Universidad Agraria del Ecuador, Av. 25 de Julio, Guayaquil 090114, Ecuador)

  • Harry Luna-Aveiga

    (Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Cdla, Universitaria Salvador Allende, Guayaquil 090514, Ecuador)

  • Rafael Valencia-García

    (Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain)

Abstract

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

Suggested Citation

  • Óscar Apolinario-Arzube & José Antonio García-Díaz & José Medina-Moreira & Harry Luna-Aveiga & Rafael Valencia-García, 2020. "Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2075-:d:448299
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/11/2075/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/8/11/2075/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2075-:d:448299. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.