@InProceedings{cliche:2017:SemEval,
  author    = {Cliche, Mathieu},
  title     = {BB\_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {573--580},
  abstract  = {In this paper we describe our attempt at producing a state-of-the-art Twitter
	sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
	Term Memory (LSTMs) networks.  Our system leverages a large amount of unlabeled
	data to pre-train word embeddings.  We then use a subset of the unlabeled data
	to fine tune the embeddings using distant supervision.                          The
	final
	CNNs
	and
	LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are
	fined tuned again.  To boost performances we ensemble several CNNs and LSTMs
	together. Our approach achieved first rank on all of the five English subtasks
	amongst 40 teams.},
  url       = {http://www.aclweb.org/anthology/S17-2094}
}