easily bult LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT
Project description
easyLDA
----------
|PyPI version|
easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
github: https://github.com/shichaoji/easyLDA
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- If you have a collection of documents, and what to explore the
relationship & topics of the docs, easyLDA is a very handy library to
use. Simply run the commend and you'll get a trained LDA model with
results visualized
The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Credit to:
https://radimrehurek.com/gensim/
http://pyldavis.readthedocs.io/en/latest/readme.html
.. |PyPI version| image:: https://badge.fury.io/py/easyLDA.svg
:target: https://badge.fury.io/py/easyLDA
installation
~~~~~~~~~~~~
``$ pip install easyLDA``
usage example
~~~~~~~~~~~~~
simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
text file (csv) sample view
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779561-dba715a0-099c-11e8-8519-09d6164e63ae.jpg" height="400px">
easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1. then choose how many topics you want the model to fit
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2. choose the topic contains only single word (1) or can be phases (2/3) as well
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
the program will be starting to train
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- in shell $ easyLDA
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779521-49237200-099c-11e8-8cb2-ed916040a526.jpg" height="400px">
model result
~~~~~~~~~~~~
models folder created by program contains the trained model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
xx.html file is the interactive visulization of the model result
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779593-cfe800c0-099d-11e8-8db5-d3431f155496.jpg" height="600px">
visualization live example
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
<h1><a href="http://shichaoji.com/2016/02/04/easylda-live-example/" target="_blank">live example</a></h1>
----------
|PyPI version|
easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
github: https://github.com/shichaoji/easyLDA
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- If you have a collection of documents, and what to explore the
relationship & topics of the docs, easyLDA is a very handy library to
use. Simply run the commend and you'll get a trained LDA model with
results visualized
The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Credit to:
https://radimrehurek.com/gensim/
http://pyldavis.readthedocs.io/en/latest/readme.html
.. |PyPI version| image:: https://badge.fury.io/py/easyLDA.svg
:target: https://badge.fury.io/py/easyLDA
installation
~~~~~~~~~~~~
``$ pip install easyLDA``
usage example
~~~~~~~~~~~~~
simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
text file (csv) sample view
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779561-dba715a0-099c-11e8-8519-09d6164e63ae.jpg" height="400px">
easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1. then choose how many topics you want the model to fit
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2. choose the topic contains only single word (1) or can be phases (2/3) as well
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
the program will be starting to train
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- in shell $ easyLDA
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779521-49237200-099c-11e8-8cb2-ed916040a526.jpg" height="400px">
model result
~~~~~~~~~~~~
models folder created by program contains the trained model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
xx.html file is the interactive visulization of the model result
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<img src="https://user-images.githubusercontent.com/20619704/35779593-cfe800c0-099d-11e8-8db5-d3431f155496.jpg" height="600px">
visualization live example
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. raw:: html
<h1><a href="http://shichaoji.com/2016/02/04/easylda-live-example/" target="_blank">live example</a></h1>
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