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        "author_email": "ClimbsBytes@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "# empathyMachines\n> A standalone NLP sentiment classifier you can import as a module\n\n## Purposes\n\n1. 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.)\n1. Have the entire sentiment prediction process scaffolded so you can feed in your own training corpus, and easily train an NLP sentiment classifier.\n\n## How to use\n\n1. `pip install empythy`\n1.\n```\nfrom empythy import EmpathyMachines\nnlp_classifier = EmpathyMachines()\nnlp_classifier.train()\nnlp_classifier.predict(text_string)\n```\n\n### Corpora included\n\n#### NLTK Movie Reviews\nThe classic sentiment corpus, 2000 movie reviews already gathered by NLTK.\n\n#### Assembling a custom Twitter sentiment corpus\n[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.\n\nI 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.\n\n\n### Train on your own corpus\n\nFeel free to train a classifier on your own corpus!\n\nTwo ways to do this\n\n1. Read in a .csv file with header row containing \"sentiment\", \"text\", and optionally, \"confidence\"\n    - Pass the name of the .csv file to train, like so:\n    - `nlp_classifier.train(corpus='custom', corpus_path='path/to/custom/corpus.csv')`\n1. Pass in an array of Python dictionaries, where each dictionary has attributes for \"sentiment\", \"text\", and optionally, \"confidence\"\n    - `nlp_classifier.train(corpus='custom', corpus_array=my_array_of_texts)`\n    - Two important parts to this, both `corpus='custom'`, and `corpus_array=my_variable_holding_the_documents`.\n\n### Advanced Usage\n1. `nlp_classifier.train(verbose=False)` to turn off print status statements while training.\n1. `nlp_classifier.train(print_analytics_results=True)` to print out results of training the classifier.",
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        "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",
        "license": "MIT",
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        "name": "empythy",
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