Metadata-Version: 1.1
Name: empythy
Version: 0.5.5
Summary: An off-the-rack NLP sentiment classifier- upload your own corpus or use the pre-installed ones
Home-page: https://github.com/ClimbsRocks/EmpathyMachines
Author: Preston Parry
Author-email: ClimbsBytes@gmail.com
License: MIT
Description: # empathyMachines
        > A standalone NLP sentiment classifier you can import as a module
        
        ## Purposes
        
        1. Offer a batteries-included NLP classifier you can use either on it's own, or to make sentiment predictions as part of a broder NLP project (for example, when classifying customer messages, whether the customer is angry or not might help you determine if this is a compensation request, or a request to adjust their address.)
        1. Have the entire sentiment prediction process scaffolded so you can feed in your own training corpus, and easily train an NLP sentiment classifier.
        
        ## How to use
        
        1. `pip install empythy`
        1. `from empythy import EmpathyMachines`
        1. `nlp_classifier = EmpathyMachines()`
        1. `nlp_classifier.train(corpus='Twitter')`
        1. `nlp_classifier.predict(text_string)`
        
        
        1. Download the repo from GitHub (pip install coming later)
        1. `cd` into repo, and `pip install -r requirements.txt`
        1. In your Python code, `from EmpathyMachines import EmpathyMachines`
        1. `nlp_classifier = EmpathyMachines()`
        1. `nlp_classifier.train(corpus='Twitter')`
        1. `nlp_classifier.predict(text_string)`
        
        
        ### Corpora included
        
        
        ### Include your own corpus (UNDER CONSTRUCTION)
        
        Feel free to train a classifier on your own corpus!
        
        Two ways to do this:
        1. Read in a .csv file with header row containing "sentiment", "text", and optionally, "confidence"
        1. Pass in an array of Python dictionaries, with attributes for "sentiment", "text", and optionally, "confidence"
        
        
        1. Create a .csv file with the following fields
        1. `nlp_classifier.train(corpus='custom', corpus_path='path/to/custom/corpus.csv', analytics_output=False)`
        
Keywords: machine learning,data science,NLP,natural language processing,sentiment,sentiment analysis,sentiment prediction,twitter corpus,twitter,tweets corpus,movie reviews corpus,NLTK,automated machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
