@InProceedings{marresetaylor-balazs-matsuo:2017:WASSA2017,
  author    = {Marrese-Taylor, Edison  and  Balazs, Jorge  and  Matsuo, Yutaka},
  title     = {Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {102--111},
  abstract  = {Video reviews are the natural evolution of written product reviews. In this
	paper we target this phenomenon and introduce the first dataset created from
	closed captions of YouTube product review videos as well as a new attention-RNN
	model for aspect extraction and joint aspect extraction and sentiment
	classification. Our model provides state-of-the-art performance on aspect
	extraction without requiring the usage of hand-crafted features on the SemEval
	ABSA corpus, while it outperforms the baseline on the joint task. In our
	dataset, the attention-RNN model outperforms the baseline for both tasks, but
	we observe important performance drops for all models in comparison to SemEval.
	These results, as well as further experiments on domain adaptation for aspect
	extraction, suggest that differences between speech and written text, which
	have been discussed extensively in the literature, also extend to the domain of
	product reviews, where they are relevant for fine-grained opinion mining.},
  url       = {http://www.aclweb.org/anthology/W17-5213}
}