@InProceedings{kumar-EtAl:2017:SemEval,
  author    = {Kumar, Abhishek  and  Sethi, Abhishek  and  Akhtar, Md Shad  and  Ekbal, Asif  and  Biemann, Chris  and  Bhattacharyya, Pushpak},
  title     = {IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text},
  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     = {894--898},
  abstract  = {This paper reports team IITPB's participation in the SemEval 2017 Task 5 on
	`Fine-grained sentiment analysis on financial microblogs and news'. We
	developed 2 systems for the two tracks. One system was based on an ensemble of
	Support Vector Classifier and Logistic Regression. This system relied on
	Distributional Thesaurus (DT), word embeddings and lexicon features to predict
	a floating sentiment value between -1 and +1. The other system was based on
	Support Vector Regression using word embeddings, lexicon features, and PMI
	scores as features. The system was ranked 5th in track 1 and 8th in track 2.},
  url       = {http://www.aclweb.org/anthology/S17-2153}
}