@InProceedings{kar-maharjan-solorio:2017:SemEval,
  author    = {Kar, Sudipta  and  Maharjan, Suraj  and  Solorio, Thamar},
  title     = {RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks},
  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     = {877--882},
  abstract  = {In this paper, we present our systems for the “SemEval-2017 Task-5 on Fine-
	Grained Sentiment Analysis on Financial Microblogs and News”. In our system,
	we combined hand-engineered lexical, sentiment and metadata features, the
	representations learned from Convolutional Neural Networks (CNN) and
	Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on
	top. With this architecture we obtained weighted cosine similarity scores of
	0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official
	scoring system, our system ranked the second place for subtask-2 and eighth
	place for the subtask-1. It ranked first for both of the subtasks by the scores
	achieved by an alternate scoring system.},
  url       = {http://www.aclweb.org/anthology/S17-2150}
}