@InProceedings{jiang-diesner:2016:COLING,
  author    = {Jiang, Ming  and  Diesner, Jana},
  title     = {Says Who…? Identification of Expert versus Layman Critics’ Reviews of Documentary Films},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2122--2132},
  abstract  = {We extend classic review mining work by building a binary classifier that
	predicts whether a review of a documentary film was written by an expert or a
	layman with 90.70% accuracy (F1 score), and compare the characteristics of the
	predicted classes. A variety of standard lexical and syntactic features was
	used for this supervised learning task. Our results suggest that experts write
	comparatively lengthier and more detailed reviews that feature more complex
	grammar and a higher diversity in their vocabulary. Layman reviews are more
	subjective and contextualized in peoples’ everyday lives. Our error analysis
	shows that laymen are about twice as likely to be mistaken as experts than vice
	versa. We argue that the type of author might be a useful new feature for
	improving the accuracy of predicting the rating, helpfulness and authenticity
	of reviews. Finally, the outcomes of this work might help researchers and
	practitioners in the field of impact assessment to gain a more fine-grained
	understanding of the perception of different types of media consumers and
	reviewers of a topic, genre or information product.},
  url       = {http://aclweb.org/anthology/C16-1200}
}