Metadata-Version: 2.1
Name: askcomm
Version: 0.0.1
Summary: A set of search patterns that query a corpus of event-based and community-detected tweets, but it could be modified to query most social-network (node-edge) data.
Home-page: https://github.com/lingeringcode/askcomm/
Author: Chris A. Lindgren
Author-email: chris.a.lindgren@gmail.com
License: UNKNOWN
Download-URL: https://github.com/lingeringcode/askcomm/
Description: # askcomm: Python 3 module - Search patterns for event-based, community-detected twitter data.
        
        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.
        
        ## Overview
        
        A set of search patterns that query a corpus of event-based and community-detected tweets, but it could be modified to query most social-network (node-edge) data. The queries are great for content produced within the detected-community subgraph data.
        
        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 as community-detected data across periodic events.
        
        ## Functions
        
        ```query_controller```: Accepts corpus and hub user data and searches for tweets germane to the detected module community across a range of periods and communities. It uses the ```find_mentions``` function to conduct a cross-reference search within a period's data range with 2 options: 'mentions_only' or 'user_and_mentions'. '```mentions_only```' searches a column with a List of mentions per tweet. '```user_and_mentions```' cross references the author  of a tweet with the list of mentions. It returns a Dict of top result tweets found during that period.
        
        ```python
        query_controller(
            hubs=df_hubs,#community-detected data
            hub_col_period='period',#column name for periods
            hub_col_module='info_module',# column name for community name
            hub_col_users='name',#column name for 
            period_range=[1,10],#range of desired periods
            module_range=[1,10],#range of desired communities/modules
            corpus=c_htg,#content corpus
            period_dates=period_dates,#List of lists with dates to 
            col_dates='dates'#column name for dates
        )
        ```
        
        ```convert_to_df```: Converts the Dict output from query_controller into a Dataframe with top result per user. If no tweet found , appends as None.
        
        ```find_ht```: Queries subset of isolated mentioned or authored tweets with hashtag group list. It returns another subset as a dataframe.
        
        ```find_links```: Queries links in tweets with search string. It returns subset as a dataframe.
        
        Other functions include: ```find_mentions``` and ```print_subset```.
        
        It functions only with Python 3.x and is not backwards-compatible.
        
        **Warning**: askcomm 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
        
        ## Installation
        
        1. Download this repo onto your computer.
        2. Store the folder in a meaningful location.
        3. Open a terminal.
        4. In the terminal, navigate to the root of the folder.
        5. In the terminal, run ```pip install .```
        
        ## 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: corpus querying,search patterns,event-based corpus
Platform: UNKNOWN
Description-Content-Type: text/markdown
