When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity

Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann, Jorma Laaksonen, Mikko Kurimo


Abstract
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.
Anthology ID:
2022.coling-1.598
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6875–6886
Language:
URL:
https://aclanthology.org/2022.coling-1.598
DOI:
Bibkey:
Cite (ACL):
Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann, Jorma Laaksonen, and Mikko Kurimo. 2022. When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6875–6886, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity (Alnajjar et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.598.pdf