Multimodal Indicators of Humor in Videos

In this paper, we propose a novel approach for generating unsupervised humor labels in videos using time-aligned user comments. We collected 100 videos and found a high agreement between our unsupervised labels and human annotations. We analyzed a set of speech, text and visual features, identifying differences between humorous and non-humorous video segments. We also conducted machine learning classification experiments to predict humor and achieved an F1-score of 0.73.