@InProceedings{mousa-schuller:2017:EACLlong,
  author    = {Mousa, Amr  and  Schuller, Bj\"{o}rn},
  title     = {Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1023--1032},
  abstract  = {Traditional learning-based approaches to sentiment analysis of written text use
	the concept of bag-of-words or bag-of-n-grams, where a document is viewed as a
	set of terms or short combinations of terms disregarding grammar rules or word
	order. Novel approaches de-emphasize this concept and view the problem as a
	sequence classification problem. In this context, recurrent neural networks
	(RNNs) have achieved significant success. The idea is to use RNNs as
	discriminative binary classifiers to predict a positive or negative sentiment
	label at every word position then perform a type of pooling to get a
	sentence-level polarity. Here, we investigate a novel generative approach in
	which a separate probability distribution is estimated for every sentiment
	using language models (LMs) based on long short-term memory (LSTM) RNNs. We
	introduce a novel type of LM using a modified version of bidirectional LSTM
	(BLSTM) called contextual BLSTM (cBLSTM), where the probability of a word is
	estimated based on its full left and right contexts. Our approach is compared
	with a BLSTM binary classifier. Significant improvements are observed in
	classifying the IMDB movie review dataset. Further improvements are achieved
	via model combination.},
  url       = {http://www.aclweb.org/anthology/E17-1096}
}