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.
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CITATION STYLE
Schwartz, H. A., Giorgi, S., Sap, M., Crutchley, P., Eichstaedt, J. C., & Ungar, L. (2017). Dlatk: Differential language analysis toolkit. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings (pp. 55–60). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-2010
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