https://github.com/trinker/sentimentpy

A Python port of the #rstats sentimentr package
https://github.com/trinker/sentimentpy

emotion nlp polarity sentiment text-mining

Last synced: 17 days ago
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A Python port of the #rstats sentimentr package

• Host: GitHub
• URL: https://github.com/trinker/sentimentpy
• Owner: trinker
• Created: 2018-11-09T01:49:08.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-11-11T01:34:38.000Z (about 6 years ago)
• Last Synced: 2024-08-06T03:02:55.203Z (3 months ago)
• Topics: emotion, nlp, polarity, sentiment, text-mining
• Language: Python
• Size: 139 KB
• Stars: 10
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSES.txt
  • Citation: CITATION.R

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README

        
sentimentpy

===========

.. image:: https://www.repostatus.org/badges/latest/wip.svg

   :alt: Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.

   :target: https://www.repostatus.org/#wip

    

.. image:: https://img.shields.io/travis/trinker/sentimentpy/master.svg?style=flat-square&logo=travis

    :target: https://travis-ci.org/trinker/sentimentpy

    :alt: Build Status

    

.. image:: bin/sentimentpy_logo/py_sentimentpyb.png

    :alt: Module Logo

    

    

**sentimentpy** is designed to quickly calculate text polarity sentiment at the sentence level.  The user can aggregate these scores by grouping variable(s) using built-in aggregate functions.  

**sentimentpy** (a Python port of the R `sentimentr package `_) is a response to my own needs with sentiment detection that were not addressed by the current **R** tools.  My own `polarity` function in the R **qdap** package is slower on larger data sets.  It is a dictionary lookup approach that tries to incorporate weighting for valence shifters (negation and amplifiers/deamplifiers).  Matthew Jockers created the `syuzhet `_ R package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods as well as a custom dictionary.  He also utilizes a wrapper for the `Stanford coreNLP `_ which uses much more sophisticated analysis.  Jocker's dictionary methods are fast but are more prone to error in the case of valence shifters.  Jocker's `addressed these critiques `_ explaining that the method is good with regard to analyzing general sentiment in a piece of literature.  He points to the accuracy of the Stanford detection as well.  In my own work I need better accuracy than a simple dictionary lookup; something that considers valence shifters yet optimizes speed which the Stanford's parser does not.  This leads to a trade off of speed vs. accuracy.  Simply, **sentimentpy** attempts to balance accuracy and speed.

Installation

============

Currently, this is a GitHub package.  To install use:

``pip install git+https://github.com/trinker/sentimentpy``

Sentence Splitting

==================

::

       

    import sentimentpy.split_sentences as ss

    

    s = [

        ' I like you.  P.S. I like carrots too mrs. dunbar. Well let\'s go to 100th st. around the corner.   ', 

        'Hello Dr. Livingstone.  How are you?', 

        'This is sill an incomplete thou.'

        

    ]

    

    ss.split_sentences(s)

::

    

   ['I like you.',

     'P.S. I like carrots too mrs. dunbar.',

     "Well let's go to 100th st. around the corner.",

     'Hello Dr. Livingstone.',

     'How are you?',

     'This is sill an incomplete thou.']

   

::

    

    x = [

        " ".join(

            ["Mr. Brown comes! He says hello. i give him coffee.  i will ",

            "go at 5 p. m. eastern time.  Or somewhere in between!go there"

        ]),

        " ".join(

            ["Marvin K. Mooney Will You Please Go Now!", "The time has come.",

            "The time has come. The time is now. Just go. Go. GO!",

            "I don't care how."

        ])

    ]

    

    ss.split_sentences(x)

::

    

    ['Mr. Brown comes!',

     'He says hello.',

     'i give him coffee.',

     'i will  go at 5 p.m. eastern time.',

     'Or somewhere in between!',

     'go there',

     'Marvin K. Mooney Will You Please Go Now!',

     'The time has come.',

     'The time has come.',

     'The time is now.',

     'Just go.',

     'Go.',

     'GO!',

     "I don't care how."]