There is a great need for an open corpus that can support investigating humor in presentations. 1 CNN-based text categorization methods have been applied for humor recognition (e.g., in (Bertero and Fung, 2016b) ) but with limitations: (a) a rigorous comparison with the state-of-the-art conventional method examined in<cite> Yang et al. (2015)</cite> is missing; (b) CNN's performance in the previous research is not quite clear 2 ; and (c) some important techniques that can improve CNN performance (e.g., using variedsized filters and dropout regularization (Hinton et al., 2012) ) were missing.