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        "author": "metalcorebear",
        "author_email": "mark.mbailey@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# NRCLex\r\n\r\n(C) 2019 Mark M. Bailey\r\n\r\n## About\r\nNRCLex will generate an affect list from a body of text.  Affect dictionary is based on the National Research Council Canada (NRC) affect lexicon (see link below).\r\n\r\nLexicon 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/)\r\n\r\n## Affects\r\nEmotional affects measured include the following:\r\n\r\n* fear\r\n* anger\r\n* anticipation\r\n* trust\r\n* surprise\r\n* positive\r\n* negative\r\n* sadness\r\n* disgust\r\n* joy\r\n\r\n## Sample Usage\r\n\r\nfrom nrclex import NRCLex\r\n\r\n*#Instantiate text object.*\r\ntext_object = NRCLex('text')\r\n\r\n*#Return words list.*\r\ntext_object.words\r\n\r\n*#Return sentences list.*\r\ntext_object.sentences\r\n\r\n*#Return affect list.*\r\ntext_object.affect_list\r\n\r\n*#Return affect dictionary.*\r\ntext_object.affect_dict\r\n\r\n*#Return affect frequencies.*\r\ntext_object.affect_frequencies\r\n\r\n",
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        "name": "NRCLex",
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