DLATK: Differential Language Analysis ToolKit

H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, Johannes Eichstaedt


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.
Anthology ID:
D17-2010
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–60
Language:
URL:
https://www.aclweb.org/anthology/D17-2010
DOI:
10.18653/v1/D17-2010
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-2010.pdf