@InProceedings{ghosal-EtAl:2017:SemEval,
  author    = {Ghosal, Deepanway  and  Bhatnagar, Shobhit  and  Akhtar, Md Shad  and  Ekbal, Asif  and  Bhattacharyya, Pushpak},
  title     = {IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial 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     = {899--903},
  abstract  = {In this  paper we propose an ensemble based model which combines state of the
	art deep learning sentiment analysis algorithms like Convolution Neural Network
	(CNN) and Long Short Term Memory (LSTM) along with feature based models to
	identify optimistic or pessimistic sentiments associated with companies and
	stocks in financial texts. We build our system to participate in a competition
	organized by Semantic Evaluation 2017 International Workshop. We combined
	predictions from various models using an artificial neural network to determine
	the opinion towards an entity in (a) Microblog Messages and (b) News Headlines
	data. Our models achieved a cosine similarity score of 0.751 and 0.697 for the
	above two tracks giving us the rank of 2nd and 7th best team respectively.},
  url       = {http://www.aclweb.org/anthology/S17-2154}
}