Evaluating Humorous Features: Towards a Humour Taxonomy

The importance of the analysis of processes related to cognitive phenomena through Natural Language Processing techniques is acquiring a greater relevance every day. Opinion Mining, Sentiment Analysis or Automatic Humour Recognition are a sample about how this kind of research works grows. In this paper we focus on the study of how the features that define a corpus of humorous data (one-liners) may be used for obtaining a set of parameters that allow us to build a primitive taxonomy of humour. We analyse, through several experiments, a set of well-known features defined in the literature, besides a set of new ones, in order to determine the importance of each one for a humour taxonomy. An evaluation of all the features was performed by means of an automatic classification task over a collection of humorous blogs. The results obtained show that some of the features may represent elemental information for the purpose of creating a humour taxonomy.