@InProceedings{poria-EtAl:2017:Long,
  author    = {Poria, Soujanya  and  Cambria, Erik  and  Hazarika, Devamanyu  and  Majumder, Navonil  and  Zadeh, Amir  and  Morency, Louis-Philippe},
  title     = {Context-Dependent Sentiment Analysis in User-Generated Videos},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {873--883},
  abstract  = {Multimodal sentiment analysis is a developing
	area of research, which involves
	the identification of sentiments in videos.
	Current research considers utterances as
	independent entities, i.e., ignores the interdependencies
	and relations among the utterances
	of a video. In this paper, we propose
	a LSTM-based model that enables
	utterances to capture contextual information
	from their surroundings in the same
	video, thus aiding the classification process.
	Our method shows 5-10% performance
	improvement over the state of the
	art and high robustness to generalizability.},
  url       = {http://aclweb.org/anthology/P17-1081}
}