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:

• Mallet for creating LDA topics
• Stanford Parser
• CMU's TweetNLP
• pandas dataframe output

Installation

DLATK is available via any of four popular installation platforms: conda, pip, github, or Docker:

New to installing Python packages?

It is recommended that you see the full installation instructions.

1. conda

conda install -c wwbp dlatk

2. pip

pip install dlatk

3. GitHub

git clone SEE_URL_ABOVE
cd dlatk
python setup.py install

4. Docker

Detailed Docker install instructions here.

docker run --name mysql_v5  --env MYSQL_ROOT_PASSWORD=my-secret-pw --detach mysql:5.5
docker run -it --rm --name dlatk_docker --link mysql_v5:mysql dlatk/dlatk bash

• DLATK at DockerHub
• DockerFile on GitHub

Dependencies

• mysqlclient
• NumPy
• scikit-learn
• SciPy
• statsmodels

See the full installation instructions for recommended and optional dependencies.

Documentation

The documentation for the latest release is at dlatk.wwbp.org.

Citation

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"
}

License

Licensed under a GNU General Public License v3 (GPLv3)

Background

Developed by the World Well-Being Project based out of the University of Pennsylvania and Stony Brook University.