MEmoFC: introducing the Multilingual Emotional Football Corpus.

This paper introduces a new corpus of paired football match reports, the Multilingual Emotional Football Corpus, (MEmoFC), which has been manually collected from English, German, and Dutch websites of individual football clubs to investigate the way different emotional states (e.g. happiness for win...

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Publicado en:Language Resources & Evaluation Vol. 55; no. 2; pp. 389 - 431
Autores principales: Braun, Nadine, van der Lee, Chris, Gatti, Lorenzo, Goudbeek, Martijn, Krahmer, Emiel
Formato: Artículo
Publicado: Springer Nature Jun2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        10.1007/s10579-020-09508-2
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          Braun, Nadine
          van der Lee, Chris
          Gatti, Lorenzo
          Goudbeek, Martijn
          Krahmer, Emiel
        affil:
          Tilburg Center for Cognition and Communication (TiCC), Tilburg University, 5037 AB, Tilburg, The Netherlands
          Human Media Interaction Lab, University of Twente, Zilverling 2082, 7500 AE, Enschede, The Netherlands
      su:
        Happiness
        Emotions
        Corpora
        Emotional state
        Soccer teams
        Soccer
      sug:
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          Happiness
          Emotions
          Corpora
          Emotional state
          Soccer teams
          Soccer
      keyword:
        Affect
        Comparable corpora
        Emotion
        Multilingual corpus
        Natural language generation
        Reportage
        Sports
      ab: This paper introduces a new corpus of paired football match reports, the Multilingual Emotional Football Corpus, (MEmoFC), which has been manually collected from English, German, and Dutch websites of individual football clubs to investigate the way different emotional states (e.g. happiness for winning and disappointment for losing) are realized in written language. In addition to the reports, it also contains the statistics for the selected matches. MEmoFC is a corpus consisting of comparable subcorpora since the authors of the texts report on the same event from two different perspectives—the winner's and the loser's side, and from an arguably more neutral perspective in tied matches. We demonstrate how the corpus can be used to investigate the influence of affect on the reports through different approaches and illustrate how game outcome influences (1) references to the own team and the opponent, and (2) the use of positive and negative emotion terms in the different languages. The MEmoFC corpus, together with the analyzed aspects of emotional language will open up new approaches for targeted automatic generation of texts.
      pubtype: Academic Journal
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    language: English
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