Tipo
              Capítulo de libro
          Año
              2016
          Lugar publicado
              Cham
          Publisher
              Springer International Publishing
          ISBN
              978-3-319-47955-2
          Páginas
              139
          Abstract
              While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics. There exist some previous works, but a characterization of humor that allows its automatic recognition and generation is far from being specified. In this work we build a crowdsourced corpus of labeled tweets, annotated according to its humor value, letting the annotators subjectively decide which are humorous. A humor classifier for Spanish tweets is assembled based on supervised learning, reaching a precision of 84 % and a recall of 69 %.
Autores
 Manuel Montes y Gómez
      
       Alberto Segura
      
       Santiago Castro
      
       Matías Cubero
      
       Hugo Jair Escalante
      
       Juan Dios de Murillo
      
      Citekey
              Castro2016
          URL a la publicación
              
          doi
              10.1007/978-3-319-47955-2_12
          Keywords
          machine learning
          humor
          computational humor
          natural language processing
              