@InProceedings{adel-chen-chen:2017:EACLshort,
  author    = {Adel, Heike  and  Chen, Francine  and  Chen, Yan-Ying},
  title     = {Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {592--598},
  abstract  = {Users often use social media to share their interest in products. We propose to
	identify purchase stages from Twitter data following the AIDA model (Awareness,
	Interest, Desire, Action). In particular, we define the task of classifying the
	purchase stage of each tweet in a user's tweet sequence. We introduce RCRNN,
	a Ranking Convolutional Recurrent Neural Network which computes tweet
	representations using convolution over word embeddings and models a tweet
	sequence with gated recurrent units. Also, we consider various methods to cope
	with the imbalanced label distribution in our data and show that a ranking
	layer outperforms class weights.},
  url       = {http://www.aclweb.org/anthology/E17-2094}
}