@InProceedings{huytien-minhle:2017:I17-1,
  author    = {Huy Tien, Nguyen  and  Minh Le, Nguyen},
  title     = {An Ensemble Method with Sentiment Features and Clustering Support},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {644--653},
  abstract  = {Deep learning models have recently been applied successfully in natural
	language processing, especially sentiment analysis. Each deep learning model
	has a particular advantage, but it is difficult to combine these advantages
	into one model, especially in the area of sentiment analysis. In our approach,
	Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) were
	utilized to learn sentiment-specific features in a freezing scheme. This
	scenario provides a novel and efficient way for integrating advantages of deep
	learning models. In addition, we also grouped documents into clusters by their
	similarity and applied the prediction score of Naive Bayes SVM (NBSVM) method
	to boost the classification accuracy of each group. The experiments show that
	our method achieves the state-of-the-art performance on two well-known
	datasets: IMDB large movie reviews for document level and Pang \& Lee movie
	reviews for sentence level.},
  url       = {http://www.aclweb.org/anthology/I17-1065}
}