@InProceedings{akhtar-EtAl:2016:COLING,
  author    = {Akhtar, Md Shad  and  Kumar, Ayush  and  Ekbal, Asif  and  Bhattacharyya, Pushpak},
  title     = {A Hybrid Deep Learning Architecture for Sentiment Analysis},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {482--493},
  abstract  = {In this paper, we propose a novel hybrid deep learning archtecture which is
	highly efficient for sentiment analysis in resource-poor languages. We learn
	sentiment embedded vectors from the Convolutional Neural Network (CNN). These
	are augmented to a set of optimized features selected through a multi-objective
	optimization (MOO) framework. The sentiment augmented optimized vector obtained
	at the end is used for the training of SVM for sentiment classification. We
	evaluate our proposed approach for coarse-grained (i.e. sentence level) as well
	as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets
	covering varying domains. In order to show that our proposed method is generic
	in nature we also evaluate it on two benchmark English datasets. Evaluation
	shows that the results of the proposed method are consistent across all the
	datasets and often outperforms the state-of-art systems. To the best of our
	knowledge, this is the very first attempt where such a deep learning model is
	used for less-resourced languages such as Hindi.},
  url       = {http://aclweb.org/anthology/C16-1047}
}