@InProceedings{saeidi-EtAl:2016:COLING,
  author    = {Saeidi, Marzieh  and  Bouchard, Guillaume  and  Liakata, Maria  and  Riedel, Sebastian},
  title     = {SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1546--1556},
  abstract  = {In this paper, we introduce the task of targeted aspect-based sentiment
	analysis.  The goal is to extract fine-grained information with respect to
	entities mentioned in user comments. This work extends both aspect-based
	sentiment analysis -- that assumes a single entity per document — and
	targeted sentiment analysis — that assumes a single sentiment towards a
	target entity. In particular, we identify the sentiment towards each aspect of
	one or more entities. As a testbed for this task, we introduce the SentiHood
	dataset, extracted from a question answering (QA) platform where urban
	neighbourhoods are discussed by users. In this context units of text often
	mention several aspects of one or more neighbourhoods. This is the first time
	that a generic social media platform,i.e.  QA, is used for fine-grained opinion
	mining.  Text coming from QA platforms are far less constrained compared to
	text from review specific platforms which current datasets are based on. We
	develop several strong baselines, relying on logistic regression and
	state-of-the-art recurrent neural networks},
  url       = {http://aclweb.org/anthology/C16-1146}
}