@InProceedings{ayata-saraclar-ozgur:2017:SemEval,
  author    = {Ayata, Deger  and  Saraclar, Murat  and  Ozgur, Arzucan},
  title     = {BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches},
  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     = {777--783},
  abstract  = {This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis
	in Twitter. We have participated in Subtask A: Message Polarity Classification
	subtask and  developed two systems. The first system  uses  word embeddings for
	feature representation and Support Vector Machine, Random Forest and Naive
	Bayes algorithms for classification of Twitter messages into negative, neutral
	and positive polarity. The second system is based on Long Short Term Memory
	Recurrent Neural Networks and uses word indexes as sequence of inputs for
	feature representation.},
  url       = {http://www.aclweb.org/anthology/S17-2131}
}