@InProceedings{gaillat-EtAl:2018:W18-31,
  author    = {Gaillat, Thomas  and  Stearns, Bernardo  and  Sridhar, Gopal  and  McDermott, Ross  and  Zarrouk, Manel  and  Davis, Brian},
  title     = {Implicit and Explicit Aspect Extraction in Financial Microblogs},
  booktitle = {Proceedings of the First Workshop on Economics and Natural Language Processing},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {55--61},
  abstract  = {This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.},
  url       = {http://www.aclweb.org/anthology/W18-3108}
}