  Prerecorded laughter accompanying dialog in comedy TV shows encourages the
audience to laugh by clearly marking humorous moments in the show. We present
an approach for automatically detecting humor in the Friends TV show using
multimodal data. Our model is capable of recognizing whether an utterance is
humorous or not and assess the intensity of it. We use the prerecorded laughter
in the show as annotation as it marks humor and the length of the audience's
laughter tells us how funny a given joke is. We evaluate the model on episodes
the model has not been exposed to during the training phase. Our results show
that the model is capable of correctly detecting whether an utterance is
humorous 78% of the time and how long the audience's laughter reaction should
last with a mean absolute error of 600 milliseconds.
