@InProceedings{morales-zhai:2017:EMNLP2017,
  author    = {Morales, Alex  and  Zhai, Chengxiang},
  title     = {Identifying Humor in Reviews using Background Text Sources},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {492--501},
  abstract  = {We study the problem of automatically identifying humorous text from a new kind
	of text data, i.e., online reviews. We propose a generative language model,
	based on the theory of incongruity, to model humorous text, which allows us to
	leverage background text sources, such as Wikipedia entry descriptions, and
	enables construction of multiple features for identifying humorous reviews.
	Evaluation of these features using supervised learning for classifying reviews
	into humorous and non-humorous reviews shows that the features constructed
	based on the proposed generative model are much more effective than the major
	features proposed in the existing literature, allowing us to achieve almost
	86\% accuracy. These humorous review predictions can also supply good
	indicators for identifying helpful reviews.},
  url       = {https://www.aclweb.org/anthology/D17-1051}
}