@InProceedings{baziotis-pelekis-doulkeridis:2017:SemEval2,
  author    = {Baziotis, Christos  and  Pelekis, Nikos  and  Doulkeridis, Christos},
  title     = {DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis},
  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     = {747--754},
  abstract  = {In this paper we present two deep-learning systems that competed at
	SemEval-2017 Task 4 “Sentiment Analysis in Twitter”. We participated in all
	subtasks for English tweets, involving message-level and topic-based sentiment
	polarity classification and quantification. We use Long Short-Term Memory
	(LSTM) networks augmented with two kinds of attention mechanisms, on top of
	word embeddings pre-trained on a big collection of Twitter messages. Also, we
	present a text processing tool suitable for social network messages, which
	performs tokenization, word normalization, segmentation and spell correction.
	Moreover, our approach uses no hand-crafted features or sentiment lexicons. We
	ranked 1st (tie) in Subtask A, and achieved very competitive results in the
	rest of the Subtasks. Both the word embeddings and our text processing tool are
	available to the research community.},
  url       = {http://www.aclweb.org/anthology/S17-2126}
}