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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2014
    • vol. 2, nº 5, march 2014
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    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2014
    • vol. 2, nº 5, march 2014
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    Graph-based Techniques for Topic Classification of Tweets in Spanish

    Autor: 
    Cordobés, Héctor
    ;
    Fernández Anta, Antonio
    ;
    Chiroque, Luis F.
    ;
    Pérez, Fernando
    ;
    Redondo, Teófilo
    ;
    Santos, Agustín
    Fecha: 
    03/2014
    Palabra clave: 
    classification; graphs; happiness; NLP; text classification; topic classification; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/9759
    DOI: 
    http://dx.doi.org/10.9781/ijimai.2014.254
    Dirección web: 
    https://www.ijimai.org/journal/node/590
    Open Access
    Resumen:
    Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy.
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