@InProceedings{pain-EtAl:2016:WNUT,
  author    = {Pain, Julie  and  Levacher, Jessie  and  Quinquenel, Adam  and  Belz, Anja},
  title     = {Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {94--101},
  abstract  = {Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs
	after release for use by the general population, but suffers from
	under-reporting and limited coverage. Automatic methods for detecting  drug
	effect reports, especially for social media, could vastly increase the scope of
	PMS. Very few automatic PMS methods are currently available, in particular for
	the messy text types encountered on Twitter. In this paper we describe first
	results for developing PMS methods specifically for tweets. We describe the
	corpus of 125,669 tweets we have created and annotated to train and test the
	tools. We find that generic tools perform well for tweet-level language
	identification and tweet-level sentiment analysis (both 0.94 F1-Score). For
	detection of effect mentions we are able to achieve 0.87 F1-Score, while
	effect-level adverse-vs.-beneficial analysis proves harder with an F1-Score of
	0.64. Among other things, our results indicate that MetaMap semantic types
	provide a very promising basis for identifying drug effect mentions in tweets.},
  url       = {http://aclweb.org/anthology/W16-3914}
}