Get Your Jokes Right: Ask the Crowd

  • Joana Costa
  • Catarina Silva
  • Mário Antunes
  • Bernardete Ribeiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6918)

Abstract

Jokes classification is an intrinsically subjective and complex task, mainly due to the difficulties related to cope with contextual constraints on classifying each joke. Nowadays people have less time to devote to search and enjoy humour and, as a consequence, people are usually interested on having a set of interesting filtered jokes that could be worth reading, that is with a high probability of make them laugh.

In this paper we propose a crowdsourcing based collective intelligent mechanism to classify humour and to recommend the most interesting jokes for further reading. Crowdsourcing is becoming a model for problem solving, as it revolves around using groups of people to handle tasks traditionally associated with experts or machines.

We put forward an active learning Support Vector Machine (SVM) approach that uses crowdsourcing to improve classification of user custom preferences. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results.

Keywords

Crowdsourcing Support Vector Machines Text Classification Humour classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Joana Costa
    • 1
  • Catarina Silva
    • 1
    • 2
  • Mário Antunes
    • 1
    • 3
  • Bernardete Ribeiro
    • 2
  1. 1.Computer Science Communication and Research Centre, School of Technology and ManagementPolytechnic Institute of LeiriaPortugal
  2. 2.Department of Informatics EngineeringCenter for Informatics and Systems of the University of Coimbra (CISUC)Portugal
  3. 3.Center for Research in Advanced Computing Systems (CRACS)Portugal

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