Differential Language Analysis ToolKit

DLATK is an end to end human text analysis package, specifically suited for social media and social scientific applications. It is written in Python 3 and developed by the World Well-Being Project at the University of Pennsylvania and Stony Brook University. It contains:

• feature extraction

• part-of-speech tagging

• correlation

• prediction and classification

• mediation

• dimensionality reduction and clustering

• wordcloud visualization

DLATK can utilize:

• HuggingFace for transformer language models

• Mallet for creating LDA topics

• Stanford Parser

• CMU's TweetNLP

• pandas dataframe output

Getting Started

• Installation
• Github Repo
• Getting started in Colab
• Tutorials
• Packaged Datasets
• dlatkInterface Flags by type
• Papers Utilizing DLATK

Citations

If you use DLATK in your work please cite the following paper:

@InProceedings{DLATKemnlp2017,
  author =  "Schwartz, H. Andrew
      and Giorgi, Salvatore
      and Sap, Maarten
      and Crutchley, Patrick
      and Eichstaedt, Johannes
      and Ungar, Lyle",
  title =   "DLATK: Differential Language Analysis ToolKit",
  booktitle =  "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
  year =    "2017",
  publisher =  "Association for Computational Linguistics",
  pages =   "55--60",
  location =   "Copenhagen, Denmark",
  url =  "http://aclweb.org/anthology/D17-2010"
}

More Information

• DLATK GitHub page

• DLATK at DockerHub

• World Well-Being Project

• Human Language Analysis Beings (HLAB) at Stony Brook

• Computational Psychology & Well-Being Lab (CPWB) at Stanford University

• dlatk

• Changelog

DLATK is licensed under a GNU General Public License v3 (GPLv3).