Metadata-Version: 2.1
Name: dlatk
Version: 1.1.8
Summary: 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. 
Home-page: http://dlatk.wwbp.org
Author: H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lukasz Dziurzynski and Megha Agrawal
Author-email: has@cs.stonybrook.edu, sgiorgi@sas.upenn.edu
License: GNU General Public License v3 (GPLv3)
Download-URL: https://github.com/dlatk/dlatk
Description: 
        # 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](http://mallet.cs.umass.edu/) for creating LDA topics
        - [Stanford Parser](http://nlp.stanford.edu/software/lex-parser.shtml) 
        - [CMU's TweetNLP](http://www.cs.cmu.edu/~ark/TweetNLP/) 
        - [pandas](http://pandas.pydata.org/) 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](http://dlatk.wwbp.org/install.html#dependencies). 
        
        ### 1. conda
        ```sh
        conda install -c wwbp dlatk
        ```
        
        ### 2. pip
        ```sh
        pip install dlatk
        ```
        
        ### 3. GitHub
        ```sh
        git clone https://github.com/dlatk/dlatk.git
        cd dlatk
        python setup.py install
        ```
        
        ### 4. Docker
        Detailed Docker install instructions [here](http://dlatk.wwbp.org/tutorials/tut_docker.html).
        
        ```sh
        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](https://hub.docker.com/r/dlatk/dlatk/)
        - [DockerFile on GitHub](https://github.com/dlatk/dlatk-docker)
        
        ## Dependencies
        - [mysqlclient](https://github.com/PyMySQL/mysqlclient-python)
        - [NumPy](http://www.numpy.org)
        - [scikit-learn](http://www.scikit-learn.org/)
        - [SciPy](http://www.scipy.org/)
        - [statsmodels](http://www.statsmodels.org/)
        
        See the [full installation instructions](http://dlatk.wwbp.org/install.html#dependencies)
        for recommended and optional dependencies.
        
        ## Documentation
        
        The documentation for the latest release is at [dlatk.wwbp.org](dlatk.wwbp.org).
        
        ## Citation
        
        If you use DLATK in your work please cite the following [paper](http://aclweb.org/anthology/D17-2010):
        
        ```
        @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)](https://www.gnu.org/licenses/gpl-3.0.en.html)
        
        ## Background
        
        Developed by the [World Well-Being Project](http://www.wwbp.org) based out of the University of Pennsylvania and Stony Brook University.
        
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
Provides-Extra: textstat
Provides-Extra: rpy2
Provides-Extra: wordcloud
Provides-Extra: jsonrpclib-pelix
Provides-Extra: image
Provides-Extra: simplejson
Provides-Extra: langid
