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Auto tags a selection of text by removing basic stop words using NLTK
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README
__init__.py
auto_tagify.py
auto_tagifytests.py

README

Auto Tagify is a simple auto tagging module that generates tags out of a selection of text. Any text that
is less than 3 characters long or matches basic stop words such as 'the', 'this', 'that', 'and', 'really', etc.
will not be included.

There are two operations Auto Tagify performs - one returns the selection of text with links embedded
in the string and the other returns a list of all the taggable words, leaving out stop words.

For the first operation, everything is optional, but it is most effective to enter some text. Optional
parameters you can set are the paths for tag links and the css classes for link.
For instance, if you set your tag routing to a relative path such as /tags/<tagged_word> and want to
use the css class named "tagged":

from auto_tagify import AutoTagify

t = AutoTagify()

t.text = "This is the text to display!"

t.link = "/tags"

t.css = "tagged"

t.generate()

The result will be: This is the <a href="/tags/text" class="tagged">text</a> to <a
href="/tags/display">display!</a>

If no link is set, the default path is "/<tagged word>", such as "/text".

For the second operation, you will only receive a list of all your taggable words from the text. You can
call it like so:

t.text = "This text is taggable taggable kittens"

t.tag_list()

The result will be a list: ['text', 'taggable', 'taggable', 'kittens']

These two operations are sufficient for you to maintain tag counts and tag references to text in your
application.

Currently works with English only.
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