In this paper, we describe a novel approach for generating unsupervised humor labels using time-aligned user comments, and predicting humor using audio information alone. We collected 241 videos of comedy movies and gameplay videos from one of the largest Chinese video-sharing websites. We generate unsupervised humor labels from laughing comments, and find high agreement between these labels and human annotations. From these unsupervised labels, we build deep learning models using speech and text features, which obtain an AUC of 0.751 in predicting humor on a manually annotated test set. To our knowledge, this is the first study predicting perceived humor in large-scale audio data.
Cite as: Yang, Z., Hu, B., Hirschberg, J. (2019) Predicting Humor by Learning from Time-Aligned Comments. Proc. Interspeech 2019, 496-500, doi: 10.21437/Interspeech.2019-3113
@inproceedings{yang19_interspeech,
author={Zixiaofan Yang and Bingyan Hu and Julia Hirschberg},
title={{Predicting Humor by Learning from Time-Aligned Comments}},
year=2019,
booktitle={Proc. Interspeech 2019},
pages={496--500},
doi={10.21437/Interspeech.2019-3113}
}