Metadata-Version: 2.1
Name: NRCLex
Version: 3.0.0
Summary: An affect generator based on TextBlob and the NRC affect lexicon.  Note that lexicon license is for research purposes only.
Home-page: https://github.com/metalcorebear/NRCLex
Author: metalcorebear
Author-email: mark.mbailey@gmail.com
License: UNKNOWN
Description: # NRCLex
        
        (C) 2019 Mark M. Bailey
        
        ## About
        NRCLex will measure emotional affect from a body of text.  Affect dictionary contains approximately 27,000 words, and is based on the National Research Council Canada (NRC) affect lexicon (see link below) and the NLTK library's WordNet synonym sets.
        
        Lexicon source is (C) 2016 National Research Council Canada (NRC) and this package is **for research purposes only**.  Source: [lexicons for research] (http://sentiment.nrc.ca/lexicons-for-research/)
        
        NLTK data is (C) 2019, NLTK Project.  Source: [NLTK] (https://www.nltk.org/).  Reference: Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O’Reilly Media Inc.
        
        ## Update
        * Expanded NRC lexicon from approximately 10,000 words to 27,000 based on WordNet synonyms.
        * Minor bug fixes.
        * Contributor updated NTC library.
        
        ## Affects
        Emotional affects measured include the following:
        
        * fear
        * anger
        * anticipation
        * trust
        * surprise
        * positive
        * negative
        * sadness
        * disgust
        * joy
        
        ## Sample Usage
        
        `from nrclex import NRCLex`<br><br>
        
        
        *#Instantiate text object (for best results, 'text' should be unicode).*<br>
        
        `text_object = NRCLex('text')`<br><br>
        
        
        *#Return words list.*<br>
        
        `text_object.words`<br><br>
        
        
        *#Return sentences list.*<br>
        
        `text_object.sentences`<br><br>
        
        
        *#Return affect list.*<br>
        
        `text_object.affect_list`<br><br>
        
        
        *#Return affect dictionary.*<br>
        
        `text_object.affect_dict`<br><br>
        
        
        *#Return raw emotional counts.*<br>
        
        `text_object.raw_emotion_scores`<br><br>
        
        
        *#Return highest emotions.*<br>
        
        `text_object.top_emotions`<br><br>
        
        
        *#Return affect frequencies.*<br>
        
        `text_object.affect_frequencies`
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=2.7
Description-Content-Type: text/markdown
