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
- Editors:
- Lucia Specia, Matt Post, Michael Paul
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 55–60
- Language:
- URL:
- https://aclanthology.org/D17-2010
- DOI:
- 10.18653/v1/D17-2010
- Cite (ACL):
- H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, and Johannes Eichstaedt. 2017. DLATK: Differential Language Analysis ToolKit. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 55–60, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- DLATK: Differential Language Analysis ToolKit (Schwartz et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D17-2010.pdf