How it works¶

I initially thought I could just search for the opposite (antonym) of my profile and search for that. It’s not that simple! What is the opposite of ‘enthusiast’ and does that even make sense?

The algorithm takes the profile description of your followers on Twitter, uses a natural language processing tool (NLTK) to understand characteristics of your followers.

The first thing to look at is the nouns in the followers description and the most common nouns. For me, this is :

{‘NN’: [(‘https’, 80),

(‘cloud’, 56), (‘@’, 39), (‘technology’, 36), (‘http’, 31), (‘software’, 30), (‘developer’, 28), (‘business’, 28), (‘world’, 26), (‘father’, 24), (‘news’, 23), (‘fan’, 21), (‘account’, 20), (‘source’, 20), (‘husband’, 20), (‘enthusiast’, 18), (‘team’, 18), (‘web’, 18), (‘geek’, 17), (‘code’, 17)], }

So what can we tell about those nouns?

We then filter out certain nouns that commonly occur, such as ‘tweet’, ‘views’, ‘opinions’, since a lot of people have a statement about their views not representing their employer etc. etc.

Once you filter that list I can see that my followers’ characteristics in a few traits:

• Their industry ‘business’, ‘technology’
• Their role ‘developer’
• Their gender ‘husband’, ‘father’
• Their interests ‘web’, ‘code’, ‘software’
• The way they describe themselves ‘geek’, ‘enthusiast’

Looking at the Proper nouns I can also get some other interesting information:

‘NNP’: [(‘@’, 313),

(‘Cloud’, 92), (‘|’, 74), (‘Data’, 63), (‘IT’, 44), (‘Dimension’, 39), (‘Software’, 36), (‘Microsoft’, 35), (‘Director’, 35), (‘Python’, 32), (‘Manager’, 31), (‘Husband’, 26), (‘Developer’, 25), (‘CTO’, 25), (‘Architect’, 25), (‘CEO’, 24), (‘Engineer’, 24), (‘/’, 24), (‘Technology’, 23), (‘Dad’, 23)],

Again filtering out some of the fluff, like @ and /

• Company data Microsoft, Dimension (Data)
• Role ‘CTO’, ‘CEO’, Engineer, Architect

Now for each of these words we build up a synset. A Synset is a set of synonyms that share a common meaning. So for ‘technology’, the synset includes the nouns ‘technology’ and ‘engineering’.

Then we look at the following characteristics of the word ‘technology’:

• The hypernyms, in this case ‘application’ and ‘profession’ (we are interested in this)
• The hyponyms (subsets), ‘aeronautical engineering’, ‘automotive technology’ ‘chemical engineering’ etc.

The diversity wheel¶

The diversity wheel has many characterstics, such as :

• gender
• background
• interests
• religion

If we put the nouns into buckets in the diversity wheel based on their hypernyms then we find a better view of your following.

Also, we don’t want to assume that all of your followers are the same, so we’ll weight each instance based on it’s frequency.

TODO¶

This is half finished (my flight leaves soon):

• build up a map of wheels and the words in those wheels (e.g professions, regligion, interests)
• map those in a chord where the size of the difference between the angles in the chord represents the difference, e.g. carpenter is very different to IT, but banker is similar to accountant
• rank all your words into the chords, show the chord diagrams and then plot points furthest away and use those words to make recommendations.

The wheels will represent the diversity wheel (http://web.jhu.edu/sebin/t/m/DiversityWheel_Small.jpg)

Credits¶

This package was created with Cookiecutter and (a fork of) the `audreyr/cookiecutter-pypackage`_ project template.

Types of Contributions¶

Get Started!¶

Ready to contribute? Here’s how to set up wntf for local development.

1. Fork the wntf repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/wntf.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv wntf
   $ cd wntf/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

   $ flake8 wntf tests
   $ python setup.py test
   $ tox
   

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check https://travis-ci.org/tonybaloney/wntf/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ python -m unittest tests.test_wntf

Development Lead¶

• Anthony Shaw <anthonyshaw@apache.org>

Contributors¶

None yet. Why not be the first?

0.1.0 (2016-04-04)¶

• First release on PyPI.