Metadata-Version: 2.1
Name: evekeys
Version: 0.0.2
Summary: A set of functions that uses sklearn to conduct a TF-IDF analysis to generate keywords from event-based / grouped textual corpus.
Home-page: https://github.com/lingeringcode/evekeys/
Author: Chris A. Lindgren
Author-email: chris.a.lindgren@gmail.com
License: UNKNOWN
Download-URL: https://github.com/lingeringcode/evekeys/
Description: # evekeys: Isolate keywords from an event-based and custom-grouped textual corpus
        
        By Chris Lindgren <chris.a.lindgren@gmail.com>
        
        Distributed under the BSD 3-clause license. See LICENSE.txt or http://opensource.org/licenses/BSD-3-Clause for details.
        
        **Documentation**: [https://evekeys.readthedocs.io/en/latest/](https://evekeys.readthedocs.io/en/latest/)
        
        ## Overview
        
        A set of functions that uses scikit-learn to conduct a TF-IDF analysis to isolate keywords from event-based documents. It answers the following questions:
        
        1. What keywords represent a particular period of content?
        2. What keywords represent a particular group of content from a particular period?
        
        It assumes you have:
        
        - imported your corpus as a pandas DataFrame,
        - included metadata information, such as a list of dates and list of groups to reorganize your corpus, and
        - pre-processed your documents.
        
        It functions only with Python 3.x and is not backwards-compatible.
        
        **Warning**: evekeys performs little to no custom error-handling, so make sure your inputs are formatted properly. If you have questions, please let me know via email.
        
        ## System requirements
        
        * pandas
        * sklearn
        * tqdm
        
        ## Installation
        ```pip install evekeys```
        
        ## Known Issues or Limitations
        
        - Please contact me if you discover any issues.
        
        ## Example notebooks
        
        - Coming soon.
        
        ## Distribution update terminal commands
        
        <pre>
        # Create new distribution of code for archiving
        sudo python setup.py sdist bdist_wheel
        
        # Distribute to Python Package Index
        python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
        </pre>
Keywords: tf-idf,keyword extraction,event-based corpus
Platform: UNKNOWN
Description-Content-Type: text/markdown
