We propose a method to predict humor response in dialog using acoustic and language features. We use data from two popular TV sitcoms — "The Big Bang Theory" and "Seinfeld" — to predict how the audience responds to humor. Due to the sequentiality of humor response in dialogues we use a Conditional Random Field as classifier/predictor. Our method is relatively effective, with a maximum precision obtained of 72.1% in "Big Bang" and 60.2% in "Seinfeld". Experiments show that audio, speed, word and sentence length features are the most effective. This work is applicable to develop appropriate machine response empathetic to emotion in dialog, in addition to humor.
['Dario Bertero', 'Pascale Fung']
Predicting humor response in dialogues from TV sitcoms