Metadata-Version: 1.1
Name: empythy
Version: 1.0.0
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
        nlp_classifier = EmpathyMachines()
        nlp_classifier.train()
        nlp_classifier.predict(text_string)
        ```
        
        ### Corpora included
        
        #### NLTK Movie Reviews
        The classic sentiment corpus, 2000 movie reviews already gathered by NLTK.
        
        #### Assembling a custom Twitter sentiment corpus
        [CrowdFlower](http://www.crowdflower.com/data-for-everyone) hosts a number of Twitter corpora that have already been graded for sentiment by panels of humans.
        
        I aggregated together 6 of their corpora into a single, aggregated and cleaned corpus, with consistent scoring labels across the entire corpus. The cleaned corpus contains over 45,000 documents, with positive, negative, and neutral sentiments.
        
        
        ### Train on your own corpus
        
        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"
            - Pass the name of the .csv file to train, like so:
            - `nlp_classifier.train(corpus='custom', corpus_path='path/to/custom/corpus.csv')`
        1. Pass in an array of Python dictionaries, where each dictionary has attributes for "sentiment", "text", and optionally, "confidence"
            - `nlp_classifier.train(corpus='custom', corpus_array=my_array_of_texts)`
            - Two important parts to this, both `corpus='custom'`, and `corpus_array=my_variable_holding_the_documents`.
        
        ### Advanced Usage
        1. `nlp_classifier.train(verbose=False)` to turn off print status statements while training.
        1. `nlp_classifier.train(print_analytics_results=True)` to print out results of training the classifier.
        
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 :: 4 - Beta
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
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
