@InProceedings{schwartz-EtAl:2017:EMNLP2017Demos,
  author    = {Schwartz, H. Andrew  and  Giorgi, Salvatore  and  Sap, Maarten  and  Crutchley, Patrick  and  Ungar, Lyle  and  Eichstaedt, Johannes},
  title     = {DLATK: Differential Language Analysis ToolKit},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {55--60},
  abstract  = {We present Differential Language Analysis Toolkit (DLATK), an
	open-source python package and command-line tool developed for conducting
	social-scientific language analyses. 
	While DLATK provides standard NLP pipeline steps such as tokenization or
	SVM-classification, its novel strengths lie in analyses useful for
	psychological, health, and social science:  
	(1) incorporation of extra-linguistic structured information, 
	(2) specified levels and units of analysis (e.g. document, user, community),
	(3) statistical metrics for continuous outcomes, and 
	(4) robust, proven, and accurate pipelines for social-scientific prediction
	problems. 
	DLATK integrates multiple popular packages (SKLearn, Mallet), enables
	interactive usage (Jupyter Notebooks), and generally follows object oriented
	principles to make it easy to tie in additional libraries or storage
	technologies.},
  url       = {http://www.aclweb.org/anthology/D17-2010}
}