Corpus ID: 2397629

Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest

@article{Radev2016HumorIC,
  title={Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest},
  author={Dragomir R. Radev and Amanda Stent and Joel R. Tetreault and Aasish Pappu and Aikaterini Iliakopoulou and Agustin Chanfreau and Paloma de Juan and Jordi Vallmitjana and Alejandro Jaimes and Rahul Jha and Robert Mankoff},
  journal={ArXiv},
  year={2016},
  volume={abs/1506.08126}
}
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the… Expand
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A robot and comedian walk into a bar, and. . . AHA!
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