@InProceedings{akhtar-EtAl:2017:EMNLP2017,
  author    = {Akhtar, Md Shad  and  Kumar, Abhishek  and  Ghosal, Deepanway  and  Ekbal, Asif  and  Bhattacharyya, Pushpak},
  title     = {A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {540--546},
  abstract  = {In this paper, we propose a novel method for combining deep learning and
	classical feature based models using a Multi-Layer Perceptron (MLP) network for
	financial sentiment analysis. We develop various deep learning models based on
	Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated
	Recurrent Unit (GRU). These are trained on top of pre-trained,
	autoencoder-based, financial word embeddings and lexicon features. An ensemble
	is constructed by combining these deep learning models and a classical
	supervised model based on Support Vector Regression (SVR). We evaluate our
	proposed technique on a benchmark dataset of SemEval-2017 shared task on
	financial sentiment analysis. The propose model shows impressive results on two
	datasets, i.e. microblogs and news headlines datasets. Comparisons show that
	our proposed model performs better than the existing state-of-the-art systems
	for the above two datasets by 2.0 and 4.1 cosine points, respectively.},
  url       = {https://www.aclweb.org/anthology/D17-1057}
}