@InProceedings{ren-zhang:2016:COLING,
  author    = {Ren, Yafeng  and  Zhang, Yue},
  title     = {Deceptive Opinion Spam Detection Using Neural Network},
  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     = {140--150},
  abstract  = {Deceptive opinion spam detection has attracted significant attention from both
	business and research communities. Existing approaches are based on manual
	discrete features, which can capture linguistic and psychological cues.
	However, such features fail to encode the semantic meaning of a document from
	the discourse perspective, which limits the performance. In this paper, we
	empirically explore a neural network model to learn document-level
	representation for detecting deceptive opinion spam. In particular, given a
	document, the model learns sentence representations with a convolutional neural
	network, which are combined using a gated recurrent neural network with
	attention mechanism to model discourse information and yield a document vector.
	Finally, the document representation is used directly as features to identify
	deceptive opinion spam. Experimental results on three domains (Hotel,
	Restaurant, and Doctor) show that our proposed method outperforms
	state-of-the-art methods.},
  url       = {http://aclweb.org/anthology/C16-1014}
}