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