@InProceedings{sidarenka-stede:2016:PEOPLES,
  author    = {Sidarenka, Uladzimir  and  Stede, Manfred},
  title     = {Generating Sentiment Lexicons for German Twitter},
  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     = {80--90},
  abstract  = {Despite a substantial progress made in developing new sentiment
	lexicon generation (SLG) methods for English, the task of
	transferring these approaches to other languages and domains in a
	sound way still remains open.  In this paper, we contribute to the
	solution of this problem by systematically comparing semi-automatic
	translations of common English polarity lists with the results of
	the original automatic SLG algorithms, which were applied directly
	to German data.  We evaluate these lexicons on a corpus of 7,992
	manually annotated tweets.  In addition to that, we also collate the
	results of dictionary- and corpus-based SLG methods in order to find
	out which of these paradigms is better suited for the inherently
	noisy domain of social media.  Our experiments show that
	semi-automatic translations notably outperform automatic systems
	(reaching a macro-averaged F1-score of 0.589), and that
	dictionary-based techniques produce much better polarity lists as
	compared to corpus-based approaches (whose best F1-scores run up
	to 0.479 and 0.419 respectively) even for the non-standard Twitter
	genre.},
  url       = {http://aclweb.org/anthology/W16-4309}
}