@InProceedings{santos-beinborn-gurevych:2016:PEOPLES,
  author    = {Santos, Pedro Bispo  and  Beinborn, Lisa  and  Gurevych, Iryna},
  title     = {A domain-agnostic approach for opinion prediction on speech},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {163--172},
  abstract  = {We explore a domain-agnostic approach for analyzing speech with the goal of
	opinion prediction. We represent the speech signal by mel-frequency cepstral
	coefficients and apply long short-term memory neural networks to automatically
	learn temporal regularities in speech. In contrast to previous work, our
	approach does not require complex feature engineering and works without textual
	transcripts. As a consequence, it can easily be applied on various speech
	analysis tasks for different languages and the results show that it can
	nevertheless be competitive to the state-of-the-art in opinion prediction. In a
	detailed error analysis for opinion mining we find that our approach performs
	well in identifying speaker-specific characteristics, but should be combined
	with additional information if subtle differences in the linguistic content
	need to be identified.},
  url       = {http://aclweb.org/anthology/W16-4318}
}